This document describes an automated attendance system based on facial recognition. The system was developed using MATLAB. It uses principal component analysis (PCA) for facial recognition and the Viola-Jones algorithm for face detection. The system captures images of a classroom, detects faces within the images, recognizes students by comparing faces to training images, and automatically updates an Excel attendance sheet to mark students as present or absent. The system aims to make the attendance process more efficient by eliminating manual roll calls and signatures.
Student management system university erpMehul Thakkar
This document is a project report submitted by Mehul Thakkar for the development of a Student Management System (SMS) as part of a University Enterprise Resource Planning (UERP) system. It includes details of the project such as an acknowledgement section, abstract, table of contents, chapters on the organizational profile, concept and fundamentals of the project, system analysis, design and development, software testing and implementation, a user manual and screen layouts, and a conclusion. The project aims to develop an integrated SMS solution to efficiently manage student information and records for a whole university in a digital format.
SCHOOL BUS ROUTING MANAGEMENT SYSTEM [FINAL]ayushi goyal
This document presents a software engineering project report on a School Bus Routing Management System. It was created by three students at Shyama Prasad Mukherji College for Women, University of Delhi, under the guidance of Dr. Baljeet Kaur. The project aims to develop a software using GIS techniques to track school buses and manage student and staff databases. Key features include student registration, login functionality for parents/guardians, and a "track" feature to monitor bus routes and locations in real-time. The waterfall model was used for the software development process.
IRJET - Implementation of Conducting Online Certification Examination in ...IRJET Journal
This document summarizes a proposed online certification examination system that would be implemented in a cloud environment. Key points:
- The current manual certification examination system has issues like being time-consuming, difficult to analyze tests and results, and prone to losing data.
- An online system using cloud computing is proposed to address these issues by automating the examination process, storing all data in databases, and allowing exams to be administered remotely.
- The proposed system would allow administrators to add exam rules, questions, and student registrations online. Students could take timed exams through the system and receive automated scoring and results. Activity during exams could also be logged and analyzed.
Smart Canteen Management System using Naïve Bayes AlgorithmIRJET Journal
This document describes a proposed smart canteen management system for educational institutions that uses machine learning. The system aims to address issues with traditional canteen operations like long wait times, inefficient processes, and poor user experience. It would allow for digital ordering and payments, inventory management, analytics and reporting. The system architecture is also outlined. The results section describes key parts of the system like the user interface, admin dashboard, registration process, and client-side ordering experience. It is concluded that the system could help reduce wait times and improve efficiency and user satisfaction compared to traditional canteen management methods.
This document summarizes a student project on developing an image-based attendance system using face recognition. It was submitted by two students, Swarup Das and Somodeep Seal, to fulfill the requirements for a Bachelor of Technology degree. The project involved building a system that can automatically detect faces in images and identify students to mark attendance. It aimed to streamline the attendance process and reduce administrative work for faculty compared to traditional paper-based methods. The document includes sections on background, methodology, implementation, results and future work. It discusses using computer vision and machine learning algorithms like Haar cascade for face detection and recognition.
This document summarizes a thesis submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The thesis proposes developing an online test system using .NET. It was submitted by three students and supervised by an assistant professor. The thesis follows a waterfall model for software development. It includes sections on software requirements specification, data flow diagrams, entity relationship diagrams, functional and non-functional requirements, testing, and screenshots of the developed system.
Attendance management system project report.Manoj Kumar
Attendance management system project report is a document in PDF file. If you have any confusion in your document then you can clear your concepts here.
Gopinath provides his resume, including educational qualifications, skills, projects, publications, conferences, and work experience. He holds an M.E. in Embedded System Technologies and B.E. in Electrical and Electronics Engineering. His areas of interest include microcontrollers and ARM processors. He has experience with C/C++, Keil C, OrCAD, and LabVIEW. Notable achievements include publications, presentations at conferences, and work as a project coordinator at HCL.
Student management system university erpMehul Thakkar
This document is a project report submitted by Mehul Thakkar for the development of a Student Management System (SMS) as part of a University Enterprise Resource Planning (UERP) system. It includes details of the project such as an acknowledgement section, abstract, table of contents, chapters on the organizational profile, concept and fundamentals of the project, system analysis, design and development, software testing and implementation, a user manual and screen layouts, and a conclusion. The project aims to develop an integrated SMS solution to efficiently manage student information and records for a whole university in a digital format.
SCHOOL BUS ROUTING MANAGEMENT SYSTEM [FINAL]ayushi goyal
This document presents a software engineering project report on a School Bus Routing Management System. It was created by three students at Shyama Prasad Mukherji College for Women, University of Delhi, under the guidance of Dr. Baljeet Kaur. The project aims to develop a software using GIS techniques to track school buses and manage student and staff databases. Key features include student registration, login functionality for parents/guardians, and a "track" feature to monitor bus routes and locations in real-time. The waterfall model was used for the software development process.
IRJET - Implementation of Conducting Online Certification Examination in ...IRJET Journal
This document summarizes a proposed online certification examination system that would be implemented in a cloud environment. Key points:
- The current manual certification examination system has issues like being time-consuming, difficult to analyze tests and results, and prone to losing data.
- An online system using cloud computing is proposed to address these issues by automating the examination process, storing all data in databases, and allowing exams to be administered remotely.
- The proposed system would allow administrators to add exam rules, questions, and student registrations online. Students could take timed exams through the system and receive automated scoring and results. Activity during exams could also be logged and analyzed.
Smart Canteen Management System using Naïve Bayes AlgorithmIRJET Journal
This document describes a proposed smart canteen management system for educational institutions that uses machine learning. The system aims to address issues with traditional canteen operations like long wait times, inefficient processes, and poor user experience. It would allow for digital ordering and payments, inventory management, analytics and reporting. The system architecture is also outlined. The results section describes key parts of the system like the user interface, admin dashboard, registration process, and client-side ordering experience. It is concluded that the system could help reduce wait times and improve efficiency and user satisfaction compared to traditional canteen management methods.
This document summarizes a student project on developing an image-based attendance system using face recognition. It was submitted by two students, Swarup Das and Somodeep Seal, to fulfill the requirements for a Bachelor of Technology degree. The project involved building a system that can automatically detect faces in images and identify students to mark attendance. It aimed to streamline the attendance process and reduce administrative work for faculty compared to traditional paper-based methods. The document includes sections on background, methodology, implementation, results and future work. It discusses using computer vision and machine learning algorithms like Haar cascade for face detection and recognition.
This document summarizes a thesis submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The thesis proposes developing an online test system using .NET. It was submitted by three students and supervised by an assistant professor. The thesis follows a waterfall model for software development. It includes sections on software requirements specification, data flow diagrams, entity relationship diagrams, functional and non-functional requirements, testing, and screenshots of the developed system.
Attendance management system project report.Manoj Kumar
Attendance management system project report is a document in PDF file. If you have any confusion in your document then you can clear your concepts here.
Gopinath provides his resume, including educational qualifications, skills, projects, publications, conferences, and work experience. He holds an M.E. in Embedded System Technologies and B.E. in Electrical and Electronics Engineering. His areas of interest include microcontrollers and ARM processors. He has experience with C/C++, Keil C, OrCAD, and LabVIEW. Notable achievements include publications, presentations at conferences, and work as a project coordinator at HCL.
Sagar Suraj Lachure is seeking a position that allows him to apply his knowledge and skills in computer science and keep up with new technologies. He has an M.Tech in computer science from Government College of Engineering, Amravati and a B.E. in IT from H.V.P.M COET, Amravati. His experience includes 6 months of teaching and working as an Assistant Professor at Yashawantrao Cavan College of Engineering since 2013. He has published several papers on topics like diabetic retinopathy detection and participated in various conferences. His skills include programming in C, C++, Java and MATLAB as well as using operating systems, databases and documentation software.
This document outlines a proposed college exam cell management system. It describes the existing manual paper-based system and its disadvantages. The proposed system would allow students to enroll and submit exam applications online through a centralized website. It would streamline the exam process and provide better convenience for students. The document then covers system design details like modules, requirements, outputs, and future enhancements.
The document describes a project report submitted by 5 students for their Bachelor of Technology degree. It outlines the development of an IIMSR Student Management System. The system will manage student records like personal details, contact details, marks details, and other functions like student/faculty profiles, marks submission, attendance records, examination results, and timetable management. It conducted a feasibility study and identified problems with the current manual system. The project aims to automate the process and make it more efficient by reducing paperwork.
The document is a project report for a Repair Shop Management System. It outlines the objectives to digitize an existing repair shop's operations and manage it through a mobile application. The proposed system allows the shop owner to handle appointments, track equipment in repair, manage inventory, and monitor repairs through their phone instead of paper records. It was developed in Java using Android Studio and SQLite for a client's repair shop to provide a simple, effective digital solution for managing the business.
Automatic liquid filling and mixing process using PLCAMIT KUMAR SINGH
In this project, a discussion about Programmable Logic Controller (PLC) application will be explained in more details and specified. This project deals with filling and mixing of different colour liquids or chemicals in bottles/ container using PLC by the application of selector switch. System is fully controlled by Bosch Rexroth IndraLogic PLC. This prototype is used in
1. Pharmaceutical Industry
2. Paint Industry
3. Food Processing Industry etc....
The literature survey discusses two papers that propose methods to improve MapReduce performance in Hadoop clusters. The first paper designs a data placement approach that initially distributes data files to nodes based on their computing capacity. It also includes algorithms to redistribute data to address skew caused by dynamic changes. The second paper presents an adaptive slot allocation mechanism called TuMM that dynamically tunes the map and reduce slot ratios based on prior job characteristics to minimize job completion times. Both papers aim to improve resource utilization and reduce job completion times in Hadoop clusters.
project synopsis face recognition attendance systemAnkitRao82
This document summarizes the proposed development of a face recognition system for student attendance. The key objectives are to automate attendance tracking through facial recognition to reduce manual errors and increase efficiency. The methodology uses image capture, face detection, preprocessing, database development and post processing. Features will be extracted from images using LBP and PCA for classification and recognition. A feasibility study found the system would be operationally and technically feasible with no need for additional hardware or software costs.
TATTI is a private vocational training institute established in 1985 in Chennai, Tamil Nadu. It offers a range of job-oriented courses across various sectors like automobile, electronics, agriculture, and more. The courses have industry-relevant syllabus and TATTI has affiliations with government bodies. It aims to provide formal education and skills to school dropouts through hands-on training and industry collaborations. TATTI has four branches in Chennai with qualified faculty and state-of-the-art infrastructure. Over 97% of students are placed in major industries through campus recruitment programs.
This document describes an online examination system that was developed to allow institutions to conduct and manage exams online. Some key points:
- The system allows creating, distributing, and grading exams through an online/networked environment rather than manually. This reduces delays, issues with paperwork, and difficulty searching/filtering records.
- It uses a client-server architecture with a web interface. Students can access practice and real exams online. Their responses are automatically graded and results distributed.
- The system aims to reduce workload for exam conductors by automating exam distribution, response collection, grading, and result processing. It also allows remote access for students and easy record management.
- Security, randomization of questions,
EMonitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
The document describes an online student attendance system project. It discusses problems with manual attendance tracking like human error and inefficiency. The proposed system automates attendance tracking using technologies like RFID and facial recognition for accuracy and real-time reporting. It allows students, faculty and administrators to access attendance records anytime on any device for transparency. The system aims to reduce errors, save time and allow analysis of attendance patterns for informed decision making.
E learning project report (Yashraj Nigam)Yashraj Nigam
This document presents a major project report on an E-Learning (Web Based Learning System) submitted in partial fulfillment of the requirements for a Bachelor of Engineering degree. The document includes a declaration signed by the three project team members confirming the originality of the work. It also includes certificates signed by the project supervisor and institute heads. The document provides an acknowledgement of the guidance and support received. It includes lists of figures and tables as well as the table of contents. It introduces the scope and objectives of the project to develop an E-Learning management system to automate processes like managing student, class, assignment, quiz and question details in order to increase efficiency and proper resource management.
The university management system is used as an digital alternative to manual system, this software is supported to eliminate and in some cases reduce the hardships faced by this existing system. The application is reduced as much as possible to avoid errors while entering the data. It also provides error message while entering invalid data. No formal knowledge is needed for the user to use this system. Thus by this all it proves it is user-friendly
This document contains the resume of Shashikant Shivaji Pawar. It summarizes his educational qualifications including a Master of Technology degree from VIT University and a Bachelor of Engineering degree from PVG's College of Engineering and Technology. It also outlines his project experience at Caterpillar India focusing on cost reduction and optimization. Additionally, it lists his software proficiencies in CAD, publications, training, extracurricular activities and personal details.
Minimalist Vintage Medieval Brown Museum Brochure (1).pdfNithishPandian1
This document announces a three-day workshop on ASIC design using Cadence tools that will be held from March 31st to April 2nd 2023. The workshop will cover various stages of the ASIC design flow from RTL design and simulation to logic synthesis, physical implementation, and timing analysis. Experts from industry and faculty from PSG College of Technology will conduct lectures and hands-on sessions. The workshop aims to provide comprehensive knowledge of the ASIC design flow to help participants understand RTL design methodology and gain expertise required for industry. It will be held at PSG College of Technology and is open to research scholars and UG/PG students from engineering and polytechnic colleges.
This document describes a coaching institute management system project submitted by Pawan Kumar and Aditya Nayak. The system was developed to automate activities at coaching institutes and provide instant information to effectively manage any coaching institute. It allows storing student, staff, and course data; tracking attendance, fees, and performance; and facilitating communication between administrators, teachers, students and parents. The project aims to save time and costs compared to a manual system while improving performance and security of data management.
The document is a project report on a Leave Management System submitted for a Master's degree. It includes an introduction outlining the need to automate existing paper-based leave management processes. It discusses the technical, economic and operational feasibility of the project. It proposes a software system with modules for teaching staff, non-teaching staff, Heads of Department and administration to manage employee leave applications and records in a centralized database.
This document provides information about the Sadguru Swami Nithyananda Institute of Technology (SSNIT). It was established in 2010 and is affiliated with Kannur University and Kerala Technological University. It is located in Kushal Nagar, Kanhangad and aims to provide quality technical education to develop skilled professionals. The institute is managed by the Shri Nithyananda Vidyakendra and offers 4-year B-Tech programs in Civil Engineering, Mechanical Engineering, Electronics and Communication Engineering, and Computer Science Engineering. It has well-equipped laboratories and facilities across 46 acres of land.
Entering College Essay. Top 10 Tips For College AdmissNat Rice
The document summarizes the discovery of a new species, Alborum Plumae, found in Mount Kosciuszko National Park in Australia. Dr. Ella Beard discovered small feathered creatures perfectly camouflaged as snow lumps on tree branches. Early analysis finds they spend most of their time stationary to avoid detection, using their wings only for escaping predators. While sharing traits with birds like down insulation and wing-aided flight, further research is needed to determine their exact taxonomic classification.
006 Essay Example First Paragraph In An ThatsnoNat Rice
The document summarizes racism depicted in two novels by John Updike: Rabbit Run and Rabbit Redux. It notes that both books show racism as an issue, with characters making racist comments about black people. Specific quotes are presented that demonstrate racist language used by characters towards black individuals. The racism portrayed is less extreme in Rabbit Redux compared to Rabbit Run. Overall, the document analyzes how Updike explores themes of racism through his famous character Harry Rabbit and the other characters in these two novels from his Rabbit series.
More Related Content
Similar to Automated Attendance System Based On Facial Recognition
Sagar Suraj Lachure is seeking a position that allows him to apply his knowledge and skills in computer science and keep up with new technologies. He has an M.Tech in computer science from Government College of Engineering, Amravati and a B.E. in IT from H.V.P.M COET, Amravati. His experience includes 6 months of teaching and working as an Assistant Professor at Yashawantrao Cavan College of Engineering since 2013. He has published several papers on topics like diabetic retinopathy detection and participated in various conferences. His skills include programming in C, C++, Java and MATLAB as well as using operating systems, databases and documentation software.
This document outlines a proposed college exam cell management system. It describes the existing manual paper-based system and its disadvantages. The proposed system would allow students to enroll and submit exam applications online through a centralized website. It would streamline the exam process and provide better convenience for students. The document then covers system design details like modules, requirements, outputs, and future enhancements.
The document describes a project report submitted by 5 students for their Bachelor of Technology degree. It outlines the development of an IIMSR Student Management System. The system will manage student records like personal details, contact details, marks details, and other functions like student/faculty profiles, marks submission, attendance records, examination results, and timetable management. It conducted a feasibility study and identified problems with the current manual system. The project aims to automate the process and make it more efficient by reducing paperwork.
The document is a project report for a Repair Shop Management System. It outlines the objectives to digitize an existing repair shop's operations and manage it through a mobile application. The proposed system allows the shop owner to handle appointments, track equipment in repair, manage inventory, and monitor repairs through their phone instead of paper records. It was developed in Java using Android Studio and SQLite for a client's repair shop to provide a simple, effective digital solution for managing the business.
Automatic liquid filling and mixing process using PLCAMIT KUMAR SINGH
In this project, a discussion about Programmable Logic Controller (PLC) application will be explained in more details and specified. This project deals with filling and mixing of different colour liquids or chemicals in bottles/ container using PLC by the application of selector switch. System is fully controlled by Bosch Rexroth IndraLogic PLC. This prototype is used in
1. Pharmaceutical Industry
2. Paint Industry
3. Food Processing Industry etc....
The literature survey discusses two papers that propose methods to improve MapReduce performance in Hadoop clusters. The first paper designs a data placement approach that initially distributes data files to nodes based on their computing capacity. It also includes algorithms to redistribute data to address skew caused by dynamic changes. The second paper presents an adaptive slot allocation mechanism called TuMM that dynamically tunes the map and reduce slot ratios based on prior job characteristics to minimize job completion times. Both papers aim to improve resource utilization and reduce job completion times in Hadoop clusters.
project synopsis face recognition attendance systemAnkitRao82
This document summarizes the proposed development of a face recognition system for student attendance. The key objectives are to automate attendance tracking through facial recognition to reduce manual errors and increase efficiency. The methodology uses image capture, face detection, preprocessing, database development and post processing. Features will be extracted from images using LBP and PCA for classification and recognition. A feasibility study found the system would be operationally and technically feasible with no need for additional hardware or software costs.
TATTI is a private vocational training institute established in 1985 in Chennai, Tamil Nadu. It offers a range of job-oriented courses across various sectors like automobile, electronics, agriculture, and more. The courses have industry-relevant syllabus and TATTI has affiliations with government bodies. It aims to provide formal education and skills to school dropouts through hands-on training and industry collaborations. TATTI has four branches in Chennai with qualified faculty and state-of-the-art infrastructure. Over 97% of students are placed in major industries through campus recruitment programs.
This document describes an online examination system that was developed to allow institutions to conduct and manage exams online. Some key points:
- The system allows creating, distributing, and grading exams through an online/networked environment rather than manually. This reduces delays, issues with paperwork, and difficulty searching/filtering records.
- It uses a client-server architecture with a web interface. Students can access practice and real exams online. Their responses are automatically graded and results distributed.
- The system aims to reduce workload for exam conductors by automating exam distribution, response collection, grading, and result processing. It also allows remote access for students and easy record management.
- Security, randomization of questions,
EMonitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
Monitor and conduct academic activities of the institute
Promote industry institution interaction as well as research & development activity
Conduct the periodic meetings of the faculties
Collaborate for Sponsored Projects of Industry
Recommend allocation of budget for the departments as requested by the Head of Departments to the Governing
The document describes an online student attendance system project. It discusses problems with manual attendance tracking like human error and inefficiency. The proposed system automates attendance tracking using technologies like RFID and facial recognition for accuracy and real-time reporting. It allows students, faculty and administrators to access attendance records anytime on any device for transparency. The system aims to reduce errors, save time and allow analysis of attendance patterns for informed decision making.
E learning project report (Yashraj Nigam)Yashraj Nigam
This document presents a major project report on an E-Learning (Web Based Learning System) submitted in partial fulfillment of the requirements for a Bachelor of Engineering degree. The document includes a declaration signed by the three project team members confirming the originality of the work. It also includes certificates signed by the project supervisor and institute heads. The document provides an acknowledgement of the guidance and support received. It includes lists of figures and tables as well as the table of contents. It introduces the scope and objectives of the project to develop an E-Learning management system to automate processes like managing student, class, assignment, quiz and question details in order to increase efficiency and proper resource management.
The university management system is used as an digital alternative to manual system, this software is supported to eliminate and in some cases reduce the hardships faced by this existing system. The application is reduced as much as possible to avoid errors while entering the data. It also provides error message while entering invalid data. No formal knowledge is needed for the user to use this system. Thus by this all it proves it is user-friendly
This document contains the resume of Shashikant Shivaji Pawar. It summarizes his educational qualifications including a Master of Technology degree from VIT University and a Bachelor of Engineering degree from PVG's College of Engineering and Technology. It also outlines his project experience at Caterpillar India focusing on cost reduction and optimization. Additionally, it lists his software proficiencies in CAD, publications, training, extracurricular activities and personal details.
Minimalist Vintage Medieval Brown Museum Brochure (1).pdfNithishPandian1
This document announces a three-day workshop on ASIC design using Cadence tools that will be held from March 31st to April 2nd 2023. The workshop will cover various stages of the ASIC design flow from RTL design and simulation to logic synthesis, physical implementation, and timing analysis. Experts from industry and faculty from PSG College of Technology will conduct lectures and hands-on sessions. The workshop aims to provide comprehensive knowledge of the ASIC design flow to help participants understand RTL design methodology and gain expertise required for industry. It will be held at PSG College of Technology and is open to research scholars and UG/PG students from engineering and polytechnic colleges.
This document describes a coaching institute management system project submitted by Pawan Kumar and Aditya Nayak. The system was developed to automate activities at coaching institutes and provide instant information to effectively manage any coaching institute. It allows storing student, staff, and course data; tracking attendance, fees, and performance; and facilitating communication between administrators, teachers, students and parents. The project aims to save time and costs compared to a manual system while improving performance and security of data management.
The document is a project report on a Leave Management System submitted for a Master's degree. It includes an introduction outlining the need to automate existing paper-based leave management processes. It discusses the technical, economic and operational feasibility of the project. It proposes a software system with modules for teaching staff, non-teaching staff, Heads of Department and administration to manage employee leave applications and records in a centralized database.
This document provides information about the Sadguru Swami Nithyananda Institute of Technology (SSNIT). It was established in 2010 and is affiliated with Kannur University and Kerala Technological University. It is located in Kushal Nagar, Kanhangad and aims to provide quality technical education to develop skilled professionals. The institute is managed by the Shri Nithyananda Vidyakendra and offers 4-year B-Tech programs in Civil Engineering, Mechanical Engineering, Electronics and Communication Engineering, and Computer Science Engineering. It has well-equipped laboratories and facilities across 46 acres of land.
Similar to Automated Attendance System Based On Facial Recognition (20)
Entering College Essay. Top 10 Tips For College AdmissNat Rice
The document summarizes the discovery of a new species, Alborum Plumae, found in Mount Kosciuszko National Park in Australia. Dr. Ella Beard discovered small feathered creatures perfectly camouflaged as snow lumps on tree branches. Early analysis finds they spend most of their time stationary to avoid detection, using their wings only for escaping predators. While sharing traits with birds like down insulation and wing-aided flight, further research is needed to determine their exact taxonomic classification.
006 Essay Example First Paragraph In An ThatsnoNat Rice
The document summarizes racism depicted in two novels by John Updike: Rabbit Run and Rabbit Redux. It notes that both books show racism as an issue, with characters making racist comments about black people. Specific quotes are presented that demonstrate racist language used by characters towards black individuals. The racism portrayed is less extreme in Rabbit Redux compared to Rabbit Run. Overall, the document analyzes how Updike explores themes of racism through his famous character Harry Rabbit and the other characters in these two novels from his Rabbit series.
The document provides instructions for requesting essay writing help from HelpWriting.net in 5 steps:
1) Create an account with a password and email.
2) Complete a 10-minute order form with instructions, sources, and deadline.
3) Choose a bid from writers based on qualifications and feedback.
4) Review the paper and authorize payment or request revisions.
5) Request multiple revisions to ensure satisfaction, with a full refund option for plagiarism.
How To Motivate Yourself To Write An Essay Write EssNat Rice
Here are the key steps to effectively roll out a new leadership philosophy and goals for success across an organization:
1. The CEO should clearly communicate the new philosophy and goals to all levels of management. This includes presenting the vision, values and expected outcomes in all-staff meetings, video conferences, and written updates.
2. Department heads and managers should then cascade the information to their direct reports. One-on-one and team meetings allow for discussion, feedback and alignment around implementation. Training may be required to ensure consistent understanding.
3. Front-line supervisors play a vital role in socializing the changes with their teams on a daily basis. Through leading by example, coaching and regular check-ins, they can reinforce
PPT - Writing The Research Paper PowerPoint Presentation, Free DNat Rice
The document provides instructions for creating an account and submitting a paper writing request on the HelpWriting.net website. It outlines a 5-step process: 1) Create an account with an email and password. 2) Complete a form with paper details, sources, and deadline. 3) Review writer bids and choose one based on qualifications. 4) Review the completed paper and authorize payment. 5) Request revisions until satisfied with the paper. The document promotes HelpWriting.net's writing services and assurances of original, high-quality content.
The document describes a significant event in the author's life where they played in an important basketball game as a 12-year-old, highlighting their preparations like putting on extra deodorant and double knotting their shoelaces. On the day of the game, the author felt intimidated by their much taller competition but was determined to play their best. The author leaves some suspense by not revealing the outcome of the game.
How To Start A Persuasive Essay Introduction - Slide ShareNat Rice
The document provides instructions for creating an account and submitting a request for writing assistance on the HelpWriting.net website. It outlines a 5-step process: 1) Create an account with an email and password. 2) Complete an order form with instructions, sources, and deadline. 3) Review bids from writers and choose one. 4) Review the completed paper and authorize payment. 5) Request revisions until satisfied with the work.
Art Project Proposal Example Awesome How To Write ANat Rice
The document outlines a five step process for requesting an assignment writing service from the website HelpWriting.net, including registering for an account, completing an order form with instructions and deadline, reviewing bids from writers and choosing one, receiving the completed paper for review, and having the option to request revisions until satisfied. The process is described as quick and simple to register, with a bidding system to choose a qualified writer within the requested deadline and guarantees of original, high-quality work or a full refund.
The Importance Of Arts And Humanities Essay.Docx - TNat Rice
The document provides instructions for using the HelpWriting.net service to have papers written. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete an order form with instructions, sources, and deadline. 3) Review bids from writers and choose one. 4) Review the completed paper and authorize payment. 5) Request revisions until satisfied. It emphasizes that original, high-quality content is guaranteed or a full refund will be provided.
For Some Examples, Check Out Www.EssayC. Online assignment writing service.Nat Rice
Here are a few key points about how language is used for social and cultural communication:
- Language allows people to communicate and interact with one another in social settings. It facilitates the sharing of ideas, information, and experiences within a culture.
- The language we learn is influenced by our culture and environment. Different cultures and communities use language in distinct ways to reflect their norms, values, and traditions.
- Oral language skills developed from an early age lay the foundation for literacy. Storybook reading and rich conversations between teachers and students help expand vocabulary and grammar.
- As students enter school, they bring their home language base - whether standard English or another variety. Teachers should build on students' existing language skills to promote
Write-My-Paper-For-Cheap Write My Paper, Essay, WritingNat Rice
This document provides instructions for submitting a paper writing request to the website HelpWriting.net. It outlines a 5-step process: 1) Create an account with an email and password. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and select one based on qualifications. 4) Review the completed paper and authorize payment if satisfied. 5) Request revisions until fully satisfied, with a refund offered for plagiarized work. The document promotes HelpWriting.net's writing services and assurances of original, high-quality content.
Printable Template Letter To Santa - Printable TemplatesNat Rice
This document provides instructions for requesting writing assistance from HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and select one based on qualifications. 4) Receive the paper and authorize payment if pleased. 5) Request revisions to ensure satisfaction, with a refund option for plagiarized content.
Essay Websites How To Write And Essay ConclusionNat Rice
The document discusses Boudicca, a Celtic queen who led a major revolt against Roman rule in Britain in AD 61. While the revolt was ultimately a military failure, it was a defining moment in British history that showed resistance to Roman domination. The revolt is primarily known through accounts by Roman historians Cornelius Tacitus and Cassius Dio, who presented the British in a negative light. However, their works remain the most credible primary sources on the events, despite obvious Roman biases.
The document provides instructions for requesting and completing an assignment writing request on the website HelpWriting.net. It outlines a 5-step process: 1) Create an account; 2) Complete an order form with instructions and deadline; 3) Review bids from writers and select one; 4) Review the completed paper and authorize payment; 5) Request revisions to ensure satisfaction. It emphasizes the original, high-quality work and refund policy if plagiarism occurs.
Hugh Gallagher Essay Hugh G. Online assignment writing service.Nat Rice
The document provides instructions for requesting writing assistance on the HelpWriting.net website. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and choose one based on qualifications. 4) Review the completed paper and authorize payment if satisfied. 5) Request revisions to ensure satisfaction, with the option of a full refund for plagiarized work.
An Essay With A Introduction. Online assignment writing service.Nat Rice
The document discusses the concept of insurgency, which refers to an armed rebellion against a constituted authority. An insurgency can be opposed through counterinsurgency warfare, measures to protect the population, and political/economic actions aimed at undermining the insurgents' claims. Insurgencies exist in an ambiguous legal and conceptual space, as not all rebellions qualify as insurgencies under international law.
How To Write A Philosophical Essay An Ultimate GuideNat Rice
The document provides guidance on writing a philosophical essay and summarizing academic texts. It outlines a 5-step process: 1) create an account, 2) complete an order form with instructions and deadline, 3) review writer bids and choose one, 4) ensure the paper meets expectations and pay the writer, 5) request revisions until satisfied. It also summarizes strategies for solving the Cuban Missile Crisis and analyzing themes in Macbeth related to ambition and self-destruction.
50 Self Evaluation Examples, Forms Questions - Template LabNat Rice
The document discusses the song "Bohemian Rhapsody" by Queen and how it broke conventions. It notes that the song has no chorus and instead has different sections like an intro, ballad, operatic passage, and hard rock section. It describes how the tempo, vocals, instrumentation, and dynamics change throughout the song, making it unique compared to typical song structure. The song brought together different musical textures and styles in a way that was groundbreaking and helped make it one of the greatest songs ever.
40+ Grant Proposal Templates [NSF, Non-ProfiNat Rice
The document discusses performance enhancing drugs in sports and argues they should be legalized. It notes their widespread current use in sports and how testing cannot detect all drug use. It also argues athletes take health risks willingly and fans want to see peak performance, so banning drugs is unreasonable and hypocritical. Legalization with regulation could better protect athlete health while satisfying fan interests.
Nurse Practitioner Essay Family Nurse Practitioner GNat Rice
The document discusses the Flapper and Gibson Girl styles that were popular for women in the late 19th and early 20th centuries. These styles represented different images of the modern woman - Flappers dressed more casually while Gibson Girls emphasized traditional femininity through corsets and accentuating their figures. Both styles nonetheless promoted women's rights and equality as women took on new social roles during this period.
Information and Communication Technology in EducationMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 2)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐈𝐂𝐓 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
8+8+8 Rule Of Time Management For Better ProductivityRuchiRathor2
This is a great way to be more productive but a few things to
Keep in mind:
- The 8+8+8 rule offers a general guideline. You may need to adjust the schedule depending on your individual needs and commitments.
- Some days may require more work or less sleep, demanding flexibility in your approach.
- The key is to be mindful of your time allocation and strive for a healthy balance across the three categories.
Artificial Intelligence (AI) has revolutionized the creation of images and videos, enabling the generation of highly realistic and imaginative visual content. Utilizing advanced techniques like Generative Adversarial Networks (GANs) and neural style transfer, AI can transform simple sketches into detailed artwork or blend various styles into unique visual masterpieces. GANs, in particular, function by pitting two neural networks against each other, resulting in the production of remarkably lifelike images. AI's ability to analyze and learn from vast datasets allows it to create visuals that not only mimic human creativity but also push the boundaries of artistic expression, making it a powerful tool in digital media and entertainment industries.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
Cross-Cultural Leadership and CommunicationMattVassar1
Business is done in many different ways across the world. How you connect with colleagues and communicate feedback constructively differs tremendously depending on where a person comes from. Drawing on the culture map from the cultural anthropologist, Erin Meyer, this class discusses how best to manage effectively across the invisible lines of culture.
How to Create a Stage or a Pipeline in Odoo 17 CRMCeline George
Using CRM module, we can manage and keep track of all new leads and opportunities in one location. It helps to manage your sales pipeline with customizable stages. In this slide let’s discuss how to create a stage or pipeline inside the CRM module in odoo 17.
How to Create User Notification in Odoo 17Celine George
This slide will represent how to create user notification in Odoo 17. Odoo allows us to create and send custom notifications on some events or actions. We have different types of notification such as sticky notification, rainbow man effect, alert and raise exception warning or validation.
Decolonizing Universal Design for LearningFrederic Fovet
UDL has gained in popularity over the last decade both in the K-12 and the post-secondary sectors. The usefulness of UDL to create inclusive learning experiences for the full array of diverse learners has been well documented in the literature, and there is now increasing scholarship examining the process of integrating UDL strategically across organisations. One concern, however, remains under-reported and under-researched. Much of the scholarship on UDL ironically remains while and Eurocentric. Even if UDL, as a discourse, considers the decolonization of the curriculum, it is abundantly clear that the research and advocacy related to UDL originates almost exclusively from the Global North and from a Euro-Caucasian authorship. It is argued that it is high time for the way UDL has been monopolized by Global North scholars and practitioners to be challenged. Voices discussing and framing UDL, from the Global South and Indigenous communities, must be amplified and showcased in order to rectify this glaring imbalance and contradiction.
This session represents an opportunity for the author to reflect on a volume he has just finished editing entitled Decolonizing UDL and to highlight and share insights into the key innovations, promising practices, and calls for change, originating from the Global South and Indigenous Communities, that have woven the canvas of this book. The session seeks to create a space for critical dialogue, for the challenging of existing power dynamics within the UDL scholarship, and for the emergence of transformative voices from underrepresented communities. The workshop will use the UDL principles scrupulously to engage participants in diverse ways (challenging single story approaches to the narrative that surrounds UDL implementation) , as well as offer multiple means of action and expression for them to gain ownership over the key themes and concerns of the session (by encouraging a broad range of interventions, contributions, and stances).
Post init hook in the odoo 17 ERP ModuleCeline George
In Odoo, hooks are functions that are presented as a string in the __init__ file of a module. They are the functions that can execute before and after the existing code.
Automated Attendance System Based On Facial Recognition
1. AUTOMATED ATTENDANCE SYSTEM BASED ON
FACIAL RECOGNITION
A PROJECT REPORT
Submitted to
VISVESVARAYA TECHNOLOGICAL UNIVERSITY
Jnana Sangama, BELAGAVI- 590018
By
Rakshitha USN: 4MW12EC059
S R Dhanush USN: 4MW12EC065
Shreeraksha Shetty USN: 4MW12EC075
Sushmitha USN: 4MW12EC088
Under the guidance of
Mr. Chetan R
Assistant Professor, Dept. of Electronics & Communication Engineering
In partial fulfillment of the requirements for the award of the degree of
Bachelor of Engineering
Department of Electronics & Communication Engineering
SHRI MADHWA VADIRAJA INSTITUTE OF TECHNOLOGY AND MANAGEMENT
Vishwothama Nagar, BANTAKAL – 574115, Udupi District
MAY 2016
2. SHRI MADHWA VADIRAJA INSTITUTE OF TECHNOLOGY AND MANAGEMENT
(A Unit of Shri Sode Vadiraja Mutt Education Trust ®, Udupi)
Vishwothama Nagar, BANTAKAL – 574 115, Udupi District, Karnataka, INDIA
Department of Electronics & Communication Engineering
CERTIFICATE
Certified that the Project Work titled ‘AUTOMATED ATTENDANCE SYSTEM
BASED ON FACIAL RECOGNITION’ is carried out by:
Ms. RAKSHITHA USN: 4MW12EC059
Mr. S R DHANUSH USN: 4MW12EC065
Ms. SHREERAKSHA SHETTY USN: 4MW12EC075
Ms. SUSHMITHA USN: 4MW12EC088
a bonafide students of Shri Madhwa Vadiraja Institute of Technology and Management, in
partial fulfillment for the award of the degree of Bachelor of Engineering in Electronics
& Communication Engineering of Visvesvaraya Technological University, Belagavi
during the year 2015-16. It is certified that all the corrections / suggestions indicated during
Internal Assessment have been incorporated in the report. The report has been approved as
it satisfies the academic requirements in respect of Project Work prescribed for the said
degree.
Mr. Chetan R Dr. Thirumaleshwara Bhat Dr. Balachandra Achar
Asst. Professor & Guide Professor & Principal Professor and HOD
Dept. of E&C Engineering SMVITM, Bantakal Dept. of E&C Engineering
Signature with date and seal:
External Viva
Name of the Examiners: Signature with Date
1.
2.
3. i
ABSTRACT
________________________________________________
Nowadays Educational institutions are concerned about regularity of student
attendance. This is mainly due to students’ overall academic performance is affected by his
or her attendance in the institute. Mainly there are two conventional methods of marking
attendance which are calling out the roll call or by taking student sign on paper. They both
were more time consuming and difficult. Hence, there is a requirement of computer-based
student attendance management system which will assist the faculty for maintaining
attendance record automatically.
In this project we have implemented the automated attendance system using
MATLAB. We have projected our ideas to implement “Automated Attendance System
Based on Facial Recognition”, in which it imbibes large applications. The application
includes face identification, which saves time and eliminates chances of proxy attendance
because of the face authorization. Hence, this system can be implemented in a field where
attendance plays an important role.
The system is designed using MATLAB platform. The proposed system uses
Principal Component Analysis (PCA) algorithm which is based on eigenface approach.
This algorithm compares the test image and training image and determines students who
are present and absent. The attendance record is maintained in an excel sheet which is
updated automatically in the system.
4. ii
ACKNOWLEDGEMENT
________________________________________________
It is our pleasure to express our heartfelt thanks to Mr. Chetan R, Assistant Professor,
Department of Electronics and Communication Engineering, for his supervision and
guidance which enabled us to understand and develop this project.
We are indebted to Prof. Dr. Thirumaleshwara Bhat, Principal, Prof. Dr. A Ganesha,
Dean (Academics) and Prof. Dr. H. V. Balachandra Achar, Head of the Department, for
their advice and suggestions at various stages of the work.
Special thanks go to the Management of Shri Madhwa Vadiraja Institute of Technology
and Management, Bantakal, Udupi for providing us with a good study environment and
laboratories facilities. Besides, we appreciate the support and help rendered by the teaching
and non-teaching staff of Electronics and Communication Engineering.
Lastly, we take this opportunity to offer our regards to all of those who have supported us
directly or indirectly in the successful completion of this project work.
Rakshitha USN 4MW12EC059
S R Dhanush USN 4MW12EC065
Shreeraksha Shetty USN 4MW12EC075
Sushmitha USN 4MW12EC088
5. iii
TABLE OF CONTENTS
________________________________________________
ABSTRACT.........................................................................................................................i
ACKNOWLEDGEMENT.................................................................................................ii
TABLE OF CONTENTS .................................................................................................iii
TABLE OF FIGURES.......................................................................................................v
1. INTRODUCTION......................................................................................................1
1.1 Motivation and Theoretical overview ...................................................................1
1.2 Problem Statement ................................................................................................1
1.3 Research objective.................................................................................................2
1.4 Scope of study.......................................................................................................2
1.5 Research Methodology..........................................................................................2
1.6 Organization of Report..........................................................................................3
2. LITERATURE SURVEY..........................................................................................4
3. FACE RECOGNITION ............................................................................................6
3.1 PCA Approach to Face Recognition..........................................................................6
3.2 Viola- Jones algorithm for face detection..................................................................9
4. PROPOSED SYSTEM ............................................................................................14
4.1 Block diagram..........................................................................................................14
5. MATLAB..................................................................................................................16
5.1 Image Processing Toolbox.......................................................................................17
5.2 Computer Vision Toolbox .......................................................................................17
5.3 Image Acquisition Toolbox .....................................................................................18
5.4 Spreadsheet Link......................................................................................................19
6. SYSTEM IMPLEMENTATION............................................................................22
6.1 System Pre-Requisites .............................................................................................23
6.2 Image Processing .....................................................................................................23
6.3 Update the Attendance sheet in Excel .....................................................................25
6.4 MATLAB Functions created ...................................................................................25
7. RESULTS AND ANALYSIS ..................................................................................27
7.2 Graphical user interface ...........................................................................................30
8. ADVANTAGES AND APPLICATIONS...............................................................32
9. FUTURE SCOPE.....................................................................................................34
7. v
TABLE OF FIGURES
________________________________________________
Figure 3-1 The integral image..............................................................................................9
Figure 3-2 Sum calculation..................................................................................................9
Figure 3-3 The different types of features .........................................................................10
Figure 3-4 The modified AdaBoost algorithm...................................................................11
Figure 3-5 The cascade classifier.......................................................................................12
Figure 4-1 Proposed system...............................................................................................14
Figure 5-1 Image Acquisition toolbox components...........................................................19
Figure 5-2 Spreadsheet Link EX toolbox ..........................................................................20
Figure 6-1 System Flowchart.............................................................................................22
Figure 6-2 Image processing procedure.............................................................................22
Figure 7-1 Collected set of training images.......................................................................27
Figure 7-2 Capturing of the classroom image and face detection .....................................28
Figure 7-3 A student is recognized and appropriate message is displayed........................29
Figure 7-4 Output obtained in the excel format (.xlsx) .....................................................30
8. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 1
Chapter 1
INTRODUCTION
1.1Motivation and Theoretical overview
In the recent years, Image processing which deals with extracting useful information from
a digital image plays a unique role in the advent of technological advancements. It
focusses on two tasks
• Improvement of pictorial information for human interpretation
• Processing of image data for storage, transmission and representation for
autonomous machine perception.
Also people have started to use image capturing devices never as before with the advent
of smart phones and closed circuit television. Since the application of image processing is
vast, extensive work and research have been carrying out in utilizing its potential to and
to make new innovative applications.
Facial recognition has been the earliest of the application derived from this technology,
which is one of the most fool proof methods in human detection. Face is a typical
multidimensional structure and needs good computational analysis for recognition.
Biometrics methods have been used for the same purpose since a long time now. Although
it is effective, it is still not completely reliable for purpose of detecting a person.
1.2Problem Statement
Attendances of every student are being maintained by every school, college and university.
Empirical evidences have shown that there is a significant correlation between students’
attendances and their academic performances. There was also a claim stated that the
students who have poor attendance records will generally link to poor retention. Therefore,
faculty has to maintain proper record for the attendance.
The manual attendance record system is not efficient and requires more time to arrange
record and to calculate the average attendance of each student. Hence there is a requirement
of a system that will solve the problem of student record arrangement and student average
attendance calculation. One alternative to make student attendance system automatic is
provided by facial recognition.
9. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 2
1.3Research objective
Face recognition can be applied for a wide variety of problems like image and film
processing, human-computer interaction, criminal identification etc. This has motivated
researchers to develop computational models to identify the faces, which are relatively
simple and easy to implement. The existing system represents some face space with higher
dimensionality and it is not effective too. The important fact which is considered is that
although these face images have high dimensionality, in reality they span very low
dimensional space. So instead of considering whole face space with high dimensionality, it
is better to consider only a subspace with lower dimensionality to represent this face space.
The goal is to implement the system (model) for a particular face and distinguish it from a
large number of stored faces with some real-time variations as well. The Eigenface
approach uses Principal Component Analysis (PCA) algorithm for the recognition of the
images. It gives us efficient way to find the lower dimensional space.
1.4Scope of study
This includes
• Face recognition algorithms.
• Image processing using MATLAB.
• Use of MATLAB toolbox such as Image acquisition toolbox and computer vision
toolbox.
• Accessing MS Excel spreadsheet in MATLAB using Spreadsheet Link EX
1.5 Research Methodology
Here we are trying to develop a system to mark attendance automatically by using
image processing technique. An efficient face recognition algorithm has to be developed
which can recognize students efficiently. Also for image processing we have to have
effective platform to test our algorithm. MATLAB gives the best set of libraries or
toolboxes for image processing programs. Also this software gives a user friendly interface
to define functions and create graphical user interface.
10. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 3
1.6Organization of Report
The proposed system is explained in chapter 4. A brief discussion of the MATLAB IDE is
given in the chapter 5. System implementation is described in chapter 6. The practical
aspects of the project, i.e., the actual results and analysis is given along with the screenshots
of the results obtained in the chapter 7.
11. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 4
Chapter 2
LITERATURE SURVEY
For our project we got motivation by the research carried out by the following people and
their published papers:
“Eigenfaces for recognition’’ (Mathew Turk and Alex Pentland) [1], here they have
developed a near-real time computer system that can locate and track a subject’s head, and
then recognize the person by comparing characteristics of the face to those of known
individuals. The computational approach taken in this system is motivated by both
physiology and information theory, as well as by the practical requirements of near-real
time performance and accuracy. This approach treats the face recognition problem as an
intrinsically two-dimensional recognition problem rather than requiring recovery of three-
dimensional geometry, taking advantage of the fact that these faces are normally upright
and thus may be described by a small set of two-dimensional characteristic views. Their
experiments show that the eigenface technique can be made to perform at very high
accuracy, although with a substantial “unknown “rejection rate and thus potentially well
suited to these applications. The future scope of this project was-in addition to recognizing
face, to use eigenface analysis to determine the gender of the subject and to interpret facial
expressions.
“Fast face recognition using eigenfaces” (Arun Vyas and Rajbala Tokas) [2], their approach
signifies face recognition as a two-dimensional problem. In this approach, face
reorganization is done by Principal Component Analysis (PCA). Face images are faced
onto a space that encodes best difference among known face images. The face space is
created by eigenface methods which are eigenvectors of the set of faces, which may not
link to general facial features such as eyes, nose, and lips. The eigenface method uses the
PCA for recognition of the images. The system performs by facing pre-extracted face image
onto a set of face space that shows significant difference among known face images. Face
will be categorized as known or unknown face after imitating it with the present database.
From the obtained results, it was concluded that, for recognition, it is sufficient to take
about 10% eigenfaces with the highest eigenvalues. It is also clear that the recognition rate
increases with the number of training images.
12. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 5
“Face recognition using eigenface approach” (Vinay Hiremath and Ashwini Mayakar) [5],
This paper is a step towards developing a face recognition system which can recognize
static images. It can be modified to work with dynamic images. In that case the dynamic
images received from the camera can first be converted in to the static ones and then the
same procedure can be applied on them. The scheme is based on an information theory
approach that decomposes face images into a small set of characteristic feature images
called ‘Eigenfaces’, which are actually the principal components of the initial training set
of face images. Recognition is performed by projecting a new image into the subspace
spanned by the Eigenfaces (‘face space’) and then classifying the face by comparing its
position in the face space with the positions of the known individuals. The Eigenface
approach gives us efficient way to find this lower dimensional space. Eigenfaces are the
Eigenvectors which are representative of each of the dimensions of this face space and they
can be considered as various face features. Any face can be expressed as linear
combinations of the singular vectors of the set of faces, and these singular vectors are
eigenvectors of the covariance matrices. The Eigenface approach for Face Recognition
process is fast and simple which works well under constrained environment. It is one of the
best practical solutions for the problem of face recognition. Many applications which
require face recognition do not require perfect identification but just low error rate. So
instead of searching large database of faces, it is better to give small set of likely matches.
By using Eigenface approach, this small set of likely matches for given images can be easily
obtained.
“Face recognition using eigenfaces and artificial neural networks” (Mayank Agarwal,
Nikunj Jain, Mr. Manish Kumar and Himanshu Agrawal) [4], this paper presents a
methodology for face recognition based on information theory approach of coding and
decoding the face image. Proposed methodology is connection of two stages – Feature
extraction using principle component analysis and recognition using the feed forward back
propagation Neural Network. The algorithm has been tested on 400 images (40 classes). A
recognition score for test lot is calculated by considering almost all the variants of feature
extraction. The proposed methods were tested on Olivetti and Oracle Research Laboratory
(ORL) face database. Test results gave a recognition rate of 97.018%
13. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 6
Chapter 3
FACE RECOGNITION
One of the simplest and most effective PCA approaches used in face recognition systems
is the so-called eigenface approach. This approach transforms faces into a small set of
essential characteristics, eigenfaces, which are the main components of the initial set of
learning images (training set). Recognition is done by projecting a new image in the
eigenface subspace, after which the person is classified by comparing its position in
eigenface space with the position of known individuals. The advantage of this approach
over other face recognition systems is in its simplicity, speed and insensitivity to small or
gradual changes on the face. The problem is limited to files that can be used to recognize
the face. Namely, the images must be vertical frontal views of human faces.
3.1 PCA Approach to Face Recognition
Principal component analysis transforms a set of data obtained from possibly correlated
variables into a set of values of uncorrelated variables called principal components. The
number of components can be less than or equal to the number of original variables. The
first principal component has the highest possible variance, and each of the succeeding
components have the highest possible variance under the restriction that it has to be
orthogonal to the previous component. We want to find the principal components, in this
case eigenvectors of the covariance matrix of facial images. The first thing we need to do
is to form a training data set. 2D image Ii can be represented as a 1D vector by concatenating
rows. Image is transformed into a vector of length N = mxn as shown in (1).
𝐼 = [
𝑥11 𝑥12 ⋯ 𝑥1𝑛
𝑥21 𝑥22 ⋯ 𝑥2𝑛
⋮ ⋮ ⋱ ⋮
𝑥𝑚1 𝑥𝑚2 ⋯ 𝑥𝑚𝑛
]
𝑚𝑥𝑛
𝐶𝑂𝑁𝐶𝐴𝑇𝐸𝑁𝐴𝑇𝐼𝑂𝑁
→
[
𝑥11
⋮
𝑥1𝑛
⋮
𝑥2𝑛
⋮
𝑥𝑚𝑛]
= 𝑥 (1)
14. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 7
Let M such vectors xi (i = 1, 2... M) of length N form a matrix of learning images, X. To
ensure that the first principal component describes the direction of maximum variance, it is
necessary to Centre the matrix. First we determine the vector of mean values Ψ, and then
subtract that vector from each image vector.
Ψ =
1
𝑀
∑ 𝑥𝑖
𝑀
𝑖=1
(2)
𝜙𝑖 = 𝑥𝑖 − Ψ (3)
Averaged vectors are arranged to form a new training matrix (size NxM).
𝐴 = [ Φ1, Φ2, Φ3, Φ4 … ] (4)
The next step is to calculate the covariance matrix C, and find its eigenvectors ei and
eigenvalues λi,
Where
𝐶 = 𝐴𝐴𝑇
(5)
𝐶 ∗ 𝑒𝑖
= 𝜆𝑖𝑒𝑖 (6)
Covariance matrix C has dimensions NxN. From that we get N eigen values and
eigenvectors. For an image size of 128x128, we would have to calculate the matrix of
dimensions 16.384x16.384 and find 16.384 eigenvectors. It is not very effective since we
do not need most of these vectors. Rank of covariance matrix is limited by the number of
images in learning set — if we have M images, we will have M–1 eigenvectors
corresponding to non-zero eigenvalues. One of the theorems in linear algebra states that the
eigenvectors ei and eigenvalues λi can be obtained by finding eigenvectors and eigenvalues
of matrix C=AT
A (dimensions MxM). If νi and μi are eigenvectors and eigen values of
matrix AT
A, eigenvector associated with the highest eigenvalue reflects the highest
variance, and the one associated with the lowest eigenvalue, the smallest variance.
Eigenvalues decrease exponentially so that about 90% of the total variance is contained in
15. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 8
the first 5% to 10% eigenvectors. Therefore, the vectors should be sorted by eigenvalues
so that the first vector corresponds to the highest eigenvalue. These vectors are then
normalized. They form the new matrix E so that each vector ei is a column vector. The
dimensions of this matrix are NXD, where D represents the desired number of eigenvectors.
It is used for projection of data matrix A and calculation of yi vectors of matrix Y = [y1, y2,
y3....yM] The matrix Y is given as:
𝑌 = 𝐸𝑇
𝐴 (7)
Each original image can be reconstructed by adding mean image Ψ to the weighted
summation of all vectors ei. The last step is the recognition of faces. Image of the person
we want to find in training set is transformed into a vector P, reduced by the mean value Ψ
and projected with a matrix of eigenvectors (eigenfaces):
𝜔 = 𝐸𝑇(𝑃 − Ψ) (8)
Classification is done by determining the distance, εi, between ω and each vector yi of
matrix Y. The most common is the Euclidean distance, but other measures may be used.
This paper presents the results for the Euclidean distance.
If A and B are two vectors of length D, the Euclidean distance between them is determined
as follows:
𝑑(𝐴, 𝐵) = √∑(𝑎𝑖 − 𝑏𝑖)2
𝐷
𝑖=1
= ||𝐴 − 𝐵|| (9)
If the minimum distance between test face and training faces is higher than a threshold θ,
the test face is considered to be unknown; otherwise it is known and belongs to the person
in the database.
S = argmini [Ɛi] (10)
The program requires a minimum distance between the test image and images from the
training base. Even if the person is not in the database, the face would be recognized. It is
therefore necessary to set a threshold that will allow us to determine whether a person is in
the database. There is no formula for determining the threshold. The most common way is
16. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 9
to first calculate the minimum distance of each image from the training base from the other
images and place that distance in a vector rast. Threshold is taken as 0.8 times of the
maximum value of vector rast:
θ = 0.8*max (rast) (11)
3.2 Viola- Jones algorithm for face detection
In 2004 an article by Paul Viola and Michael J. Jones titled [3] “Robust Real-Time Face
Detection” was publish in the International Journal of Computer Vision. The algorithm
presented in this article has been so successful that today it is very close to being the de
facto standard for solving face detection tasks. This success is mainly attributed to the
relative simplicity, the fast execution and the remarkable performance of the algorithm.
3.2.1 The scale invariant detector
The first step of the Viola-Jones face detection algorithm is to turn the input image into an
integral image. This is done by making each pixel equal to the entire sum of all pixels above
and to the left of the concerned pixel. This is demonstrated in Figure 3-1.
Figure 3-1 The integral image
This allows for the calculation of the sum of all pixels inside any given rectangle using only
four values. These values are the pixels in the integral image that coincide with the corners
of the rectangle in the input image. This is demonstrated in Figure 3-2.
Figure 3-2 Sum calculation
17. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 10
Since both rectangle B and C include rectangle A, the sum of A has to be added to the
calculation.
It has now been demonstrated how the sum of pixels within rectangles of arbitrary size can
be calculated in constant time. The Viola-Jones face detector analyzes a given sub-window
using features consisting of two or more rectangles. The different types of features are
shown in Figure 3-3.
Figure 3-3 The different types of features
Each feature results in a single value which is calculated by subtracting the sum of the white
rectangle(s) from the sum of the black rectangle(s).
Viola-Jones have empirically found that a detector with a base resolution of 24*24 pixels
gives satisfactory results. When allowing for all possible sizes and positions of the features
in Figure 4 a total of approximately 160.000 different features can then be constructed.
Thus, the amount of possible features vastly outnumbers the 576 pixels contained in the
detector at base resolution. These features may seem overly simple to perform such an
advanced task as face detection, but what the features lack in complexity they most
certainly have in computational efficiency.
One could understand the features as the computer’s way of perceiving an input image. The
hope being that some features will yield large values when on top of a face. Of course
operations could also be carried out directly on the raw pixels, but the variation due to
different pose and individual characteristics would be expected to hamper this approach.
The goal is now to smartly construct a mesh of features capable of detecting faces and this
is the topic of the next section.
3.2.2 The modified AdaBoost algorithm
AdaBoost [3] is a machine learning boosting algorithm capable of constructing a strong
classifier through a weighted combination of weak classifiers. (A weak classifier classifies
18. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 11
correctly in only a little bit more than half the cases.) To match this terminology to the
presented theory each feature is considered to be a potential weak classifier.
Figure 3-4 The modified AdaBoost algorithm
An important part of the modified AdaBoost algorithm is the determination of the best
feature, polarity and threshold. There seems to be no smart solution to this problem and
Viola-Jones suggest a simple brute force method. This means that the determination of each
new weak classifier involves evaluating each feature on all the training examples in order
to find the best performing feature. This is expected to be the most time consuming part of
the training procedure.
The best performing feature is chosen based on the weighted error it produces. This
weighted error is a function of the weights belonging to the training examples. As seen in
Figure 3-4 part 4, the weight of a correctly classified example is decreased and the weight
of a misclassified example is kept constant. As a result, it is more ‘expensive’ for the second
feature (in the final classifier) to misclassify an example also misclassified by the first
feature, than an example classified correctly. An alternative interpretation is that the second
feature is forced to focus harder on the examples misclassified by the first. The point being
that the weights are a vital part of the mechanics of the AdaBoost algorithm.
19. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 12
With the integral image, the computationally efficient features and the modified AdaBoost
algorithm in place it seems like the face detector is ready for implementation, but Viola-
Jones have one more ace up the sleeve.
3.2.3 The cascaded classifier
The basic principle of the Viola-Jones face detection algorithm is to scan the detector many
times through the same image – each time with a new size. Even if an image should contain
one or more faces it is obvious that an excessive large amount of the evaluated sub-windows
would still be negatives (non-faces). This realization leads to a different formulation of the
problem:
Instead of finding faces, the algorithm should discard non-faces.
The thought behind this statement is that it is faster to discard a non-face than to find a face.
With this in mind a detector consisting of only one (strong) classifier suddenly seems
inefficient since the evaluation time is constant no matter the input. Hence the need for a
cascaded classifier arises.
The cascaded classifier is composed of stages each containing a strong classifier. The job
of each stage is to determine whether a given sub-window is definitely not a face or maybe
a face. When a sub-window is classified to be a non-face by a given stage it is immediately
discarded. Conversely a sub-window classified as a maybe-face is passed on to the next
stage in the cascade. It follows that the more stages a given sub-window passes, the higher
the chance the sub-window actually contains a face. The concept is illustrated with two
stages in Figure 3-5.
Figure 3-5 The cascade classifier
In a single stage classifier one would normally accept false negatives in order to reduce the
false positive rate. However, for the first stages in the staged classifier false positives are
not considered to be a problem since the succeeding stages are expected to sort them out.
20. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 13
Therefore, Viola-Jones prescribe the acceptance of many false positives in the initial stages.
Consequently, the amount of false negatives in the final staged classifier is expected to be
very small.
Viola-Jones also refer to the cascaded classifier as an attentional cascade. This name
implies that more attention (computing power) is directed towards the regions of the image
suspected to contain faces. It follows that when training a given stage, say n, the negative
examples should of course be false negatives generated by stage n-1.
21. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 14
Chapter 4
PROPOSED SYSTEM
The present system of attendance marking i.e., manually calling out the roll call by the
faculty have quite satisfactorily served the purpose. With the change in the educational
system with the introduction of new technologies in classroom such as virtual classroom,
the traditional way of taking attendance may not be viable anymore. Even with rising
number of course of study offered by universities, processing of attendance manually could
be time consuming. Hence, in our project we aim at creating a system to take attendance
using facial recognition technology in classrooms and creating an efficient database to
record them.
4.1 Block diagram
Figure 4-1 Proposed system
The block diagram in figure 4-1 describes the proposed system for Face Recognition based
Classroom attendance system. The system requires a camera installed in the classroom at a
position where it could capture all the students in the classroom and thus capture their
images effectively. This image is processed to get the desired results. The working is
explained in brief below:
22. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 15
• Capturing Camera: Camera is installed in a classroom to capture the face of the
student. The camera has to be places such that it captures the face of all the students
effectively. This camera has to be interfaced to computer system for further
processing either through a wired or a wireless network. In our prototype we use
the in-built camera of the laptop.
• Image Processing: Facial recognition algorithm is applied on the captured image.
The image is cropped and stored for processing. The module recognizes the images
of the students face which have been registered manually with their names and ID
codes in the database. We use MATLAB for all the image processing and
acquisition operations. The whole process requires the following steps:
a) Train Database: Initially we take facial image of the enrolled students. In our
system we have taken three images each. This data is used later used in the facial
recognition algorithm. It is done using Image Acquisition Toolbox of the
MATLAB. All the cropped image of the face is resized to a 240 X 300 image.
b) Face Detection and cropping: The captured image of the classroom is initially
scanned to detect faces. This is done using Computer Vision Toolbox by the
function vision.CascadeObjectDetector(). This function work on the basis of
Viola-Jones algorithm. This algorithm focusses more on speed and reliability.
The detected faces are cropped and resized to a 240 X 300 image, same as the
train database.
c) Face Recognition: For recognition, the feature locations are refined and the
face is normalized with eyes and month in fixed locations. Images from the face
tracker are used to train a frontal Eigen space, and the leading three eigenvectors
are retained. Since the face images have been warped into frontal views a single
eigen space is enough. Face recognition is then performed using the Eigen face
approach with additional temporal information added. The projection
coefficients of all images of each person are modelled as a Gaussian distribution
and the face is classified based on the probability of match.
d) Attendance Recording: We use Excel spreadsheet to store the recorded
attendance for easy-to-use output format, which is also the software which is
familiar to majority of the institution staffs. This is done using Spreadsheet Link
EX toolbox. If a student is recognized, the corresponding cell is updated with
‘1’, else a ‘0’. Using the formatting in the Excel, we can effectively retrieve the
information effectively.
23. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 16
Chapter 5
MATLAB
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and
fourth-generation programming language. A proprietary programming language developed
by Math Works, MATLAB allows matrix manipulations, plotting of functions and data,
implementation of algorithms, creation of user interfaces, and interfacing with programs
written in other languages, including C, C++, Java, Fortran and Python.
Although MATLAB is intended primarily for numerical computing, an optional toolbox
uses the Mu PAD symbolic engine, allowing access to symbolic computing abilities. An
additional package, Simulink, adds graphical multi-domain simulation and model-based
design for dynamic and embedded systems.
The basic data structure in MATLAB is the array, an ordered set of real or complex
elements. This object is naturally suited to the representation of images, real-valued ordered
sets of color or intensity data.
MATLAB stores most images as two-dimensional arrays (i.e., matrices), in which each
element of the matrix corresponds to a single pixel in the displayed image. (Pixel is derived
from picture element and usually denotes a single dot on a computer display.) For example,
an image composed of 200 rows and 300 columns of different coloured dots would be
stored in MATLAB as a 200-by-300 matrix. Some images, such as RGB, require a three-
dimensional array, where the first plane in the third dimension represents the red pixel
intensities, the second plane represents the green pixel intensities, and the third plane
represents the blue pixel intensities.
This convention makes working with images in MATLAB similar to working with any
other type of matrix data, and makes the full power of MATLAB available for image
processing applications. For example, you can select a single pixel from an image
matrix using normal matrix subscripting.
24. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 17
5.1 Image Processing Toolbox
The Image Processing Toolbox is a collection of functions that extend the capability of the
MATLAB® numeric computing environment. The toolbox supports a wide range of image
processing operations, including:
•Spatial image transformations
•Morphological operations
•Neighborhood and block operations
•Linear filtering and filter design
•Transforms
•Image analysis and enhancement
•Image registration
•De blurring
•Region of interest operations
Many of the toolbox functions are MATLAB M-files, a series of MATLAB statements that
implement specialized image processing algorithms. You can extend the capabilities of the
Image Processing Toolbox by writing your own M-files, or by using the toolbox in
combination with other toolboxes, such as the Signal Processing Toolbox and the Wavelet
Toolbox.
5.2 Computer Vision Toolbox
The field is of importance for such various applications as autonomous vehicles, navigating
with the help of images, captured by a mounted camera, and high precision measurements
using images, ta- ken by calibrated cameras. In this paper, we will present a number of
numerical routines, implemented in MATLAB, that are useful in a variety of computer
vision applications. The collection of routines will be called the Computer Vision Toolbox.
One of the main problems in Computer Vision is to calculate the 3D-structure of the scene
and the motion of the camera from measurements in the images taken from different view-
points in the scene. This problem is called structure and motion, referring to the fact that
both the structure of the scene and the motion of the camera are calculated from image
measurements only. A number of different sub problems, arising from different knowledge
of the intrinsic properties of the camera appear. Other important problems are to calculate
the structure of the 3D-scene given the motion of the camera and to calculate the motion of
25. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 18
the camera given the structure of the scene. These problems are somewhat simpler than the
general structure and motion problem, but are nevertheless important for navigation and
obstacle avoidance. A related problem is to calibrate a camera, i.e. calculate the focal
distance, the principal point etc., from images of a known scene. This calibration may be
used to make a more precise reconstruction. The problem of estimating structure and
motion from image sequences are closely related to the field of closed range
photogrammetry. In this field images are used to make precise measurements of three-
dimensional objects from a number of images, often taken by calibrated cameras. The main
difference is that in computer vision, the focus is not on accuracy, but instead on reliability
and speed of calculation. In all these problems different kind of so called features can be
used. The simplest feature is points that are easily detected in the sequence, such as corners
or pain- ted marks. Other types of features are lines and curves, which are easier to detect
and track but more difficult to use in structure and motion algorithms. In order to use our
computer vision routines these features must be detected in advance and the
correspondence between different images must be established. The main emphasis of our
computer vision toolbox is to use these detected features to solve for structure and motion.
5.3 Image Acquisition Toolbox
The Image Acquisition Toolbox as in figure 5-1, is a collection of functions that extend the
capability of the MATLAB® numeric computing environment. The toolbox supports a
wide range of image acquisition operations, including
• Acquiring images through many types of image acquisition devices, from
professional grade frame grabbers to USB-based Webcams
• Viewing a preview of the live video stream
• Triggering acquisitions (includes external hardware triggers
• Configuring call back functions that execute when certain events occur
• Bringing the image data into the MATLAB workspace
Many of the toolbox functions are MATLAB M-files. You can extend the capabilities of
the Image Acquisition Toolbox by writing your own M-files, or by using the toolbox in
combination with other toolboxes, such as the Image Processing Toolbox and the Data
Acquisition Toolbox. The toolbox also includes a Simulink® interface called the Image
Acquisition Block set. This block set extends Simulink with a block that lets you bring live
video data into a model.
26. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 19
Figure 5-1 Image Acquisition toolbox components
5.4 Spreadsheet Link
The Spreadsheet Link EX software Add-In integrates the Microsoft® Excel® and
MATLAB® products in a computing environment running Microsoft® Windows®. It
connects the Excel® interface to the MATLAB workspace, enabling you to use Excel work
sheet and macro programming tools to leverage the numerical, computational, and
graphical power of MATLAB.
You can use Spreadsheet Link EX functions in an Excel work sheet or macro to exchange
and synchronize data between Excel and MATLAB, without leaving the Excel
27. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 20
environment. With a small number of functions to manage the link and manipulate data,
the Spreadsheet Link EX software is powerful in its simplicity.
The Spreadsheet Link EX software supports MATLAB two-dimensional numeric arrays,
one-dimensional character arrays (strings), and two-dimensional cell arrays. It does not
work with MATLAB multidimensional arrays and structures.
Figure 5-2 Spreadsheet Link EX toolbox
5.5 GUI
MATLAB is built around a programming language, and as such it’s really designed with
tool-building in mind. Guide extends MATLAB’s support for rapid coding into the realm
of building GUIs. Guide is a set of MATLAB tools designed to make building GUIs easier
and faster. Just as writing math in MATLAB is much like writing it on paper, building a
GUI with Guide is much like drawing one on paper. As a result, you can lay out a complex
graphical tool in minutes. Once your buttons and plots are in place, the Guide Callback
Editor lets you set up the MATLAB code that gets executed when a particular button is
pressed.
Modifying Properties with the Property Editor
The five tools that together make up Guide are:
•The Property Editor
•The Guide Control Panel
•The Callback Editor
•The Alignment Tool
28. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 21
•The Menu Editor
Simplicity
Simplicity in design is our chief goal. A simple GUI has a clean look and a sense of unity.
It’s very easy to add functionality to the GUI you’re building, but if that functionality really
doesn’t belong, take it out. Avoid screen clutter, and only present users with choices that
advance them toward the completion of the task.
Emphasize Form, not Number
Clutter obscures valuable information. Since visualization is inherently more qualitative
than quantitative, concentrate on the shape and let the labelling vanish. Once you let
yourself remove a piece of the GUI that doesn’t absolutely need to be there, you may find
that you can eliminate a lot of supporting machinery that no longer has any purpose.
Minimize the Area of Interaction
Don’t use two figures when one will do. If you’re demonstrating input-output
relationships, put the input right next to the output. The grid lines on the left don’t really
add value to the image.
29. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 22
Chapter 6
SYSTEM IMPLEMENTATION
System Flowchart
Figure 6-1 System Flowchart
IMAGE PROCESSING
Figure 6-2 Image processing procedure
30. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 23
6.1 System Pre-Requisites
The first step in implementing the system is to create a database of enrolled students’
database. In actual implementation this step must be a part of the admission process where
we collect the necessary information of the students.
This set of images is referred to as train database for the algorithm. The facial recognition
algorithm (here we use Eigenfaces method), then uses the database to calculate the
eigenfaces for face recognition.
In our project we have created a function ‘training.m’ for this purpose. This function works
as follows-
• Capture the image of the student
• Using the function ‘visionCascadeObjectdetector’ of the Computer vision toolbox,
detect the face from the image. This function works on the basis of Viola-Jones
algorithm.
• The detected faces are cropped and saved in the database.
This function has to be in the folder where the main code is saved. Also the train database
is saved in the folder ‘TrainDatabase’ which is also stored in the same folder for best results.
After this step the system is ready for recording the attendance of the registered students.
6.2 Image Processing
This is the most important part of the system since it is based in the concept of image
processing itself. This process is explained the sequence of its occurrence in the flowchart
under the title image processing.
6.2.1 Capture Image
The image of the classroom is captured such that the faces of all the students are captured
efficiently. This image is used for further processing of our algorithm. We have used the
laptop camera with a resolution of 1366x768 itself since for the prototype this resolution
is sufficient. For more accurate processing of a larger classroom, we need to use camera
with higher resolution.
31. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 24
6.2.2 Face Detection and Cropping
The image captured image is read in MATLAB. The image is nothing but a matrix of
numbers which correspond to the pixel values. The software doesn’t know where in this
collection of numbers the faces are present, which are the input for our algorithm. Thus,
face detection performs this task.
We use the function ‘vision.CascadeObjectDetector()’ of the Computer Vision Toolbox for
the same. This function detects the face based on the Viola-Jones algorithm whose
description is given in the appendix. This sequence of steps in this algorithm is as follows.
• Read the image captured in the previous step
• The faces are detected from the above image as explained earlier
• We crop the area of the image where the faces are marked and saved into a
folder as individual image files in JPEG format.
The algorithm detects all the faces clearly visible in the captured image of the classroom.
Each student need to be in an upright right position to avoid exclusion of their presence by
the system.
6.2.3 Face Recognition using Eigenfaces
We have used Eigenfaces algorithm for face recognition in the project. This is a fast and
cost effective solution for face recognition giving an appreciable level of accuracy.
The two dimensional images in training data set are converted into a one-dimensional
vector.
• Several such vectors form a matrix of learning images
• We determine the vector of mean values and subtract that vector from each
image vector
• These average vectors are arranged to form a new training matrix.
• We calculate the covariance matrix from which we get the Eigen values
and Eigen vectors.
• Eigen vectors associated with highest Eigen values reflects the highest
variance and vice-versa
• Therefore, the vector should be stored by Eigen values so that the first
vector corresponds to highest Eigen value. The vectors are normalized.
32. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 25
• The image of the person we want to find in the training set is transformed
into a vector, reduced by the mean value and projected with the matrix of
the Eigen vector.
• Classification is done by determining the Euclidean distance between the
two vectors of the images of the training data set and the test image
• If the minimum distance between the test face and training face is less than
the threshold. It is considered to be known and belong to the person in the
database otherwise it is considered to be unknown.
Whenever a person is successfully recognized the system automatically marks his or her
attendance in the database which is in the MS Excel. This is explained in the following
section.
6.2.4 Store Recognized Entries
Whenever the algorithm finds a match, we update the corresponding field of the person in
the excel sheet with a ‘1’ on that particular date. Else by default it is marked as ‘0’ which
says that the person is absent. MS Excel provides a very efficient way of storing the data.
This is explained in the next section.
6.3 Update the Attendance sheet in Excel
The MATLAB IDE and the MS Excel sheet is linked using the toolbox Spreadsheet Link
Ex. Whenever a detected face matches with a person in the database, the value is updated
in that particular Excel sheet. This is carried out through the function xlswrite().
• The candidate’s identity is determined through the index of the image with which
the detected face matches with.
• A spreadsheet of the desired format has to be drafted beforehand (attendance.xls
in our system)
• Using the index values corresponding cell in the sheet is updated with one along
with the time and date of the classroom
6.4 MATLAB Functions created
We have created program modules or functions for each of the blocks we have discussed
previously, in the standard MATLAB fie, i.e., in ‘.m’ format. This helps us in clearly
33. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 26
defining the different aspects of the program clearly, easier debugging and in the later
phase, the creation Graphical User Interface (GUI) also. The following are the modules we
have created and its description.
a. Training.m – Capture a predefined number of images of the candidate (in our case
its three), crop the face and store the image in the TrainDatabase folder
automatically.
b. Face_detection.m – After the camera captures the students’ image in the
classroom, this function detects the faces of the student, crops them and uses these
variables as the input argument for the function which does the face recognition
part. Initially this module also serves as the main program in our code.
c. Taking_Snapshot.m – The laptop camera is initialized and starts capturing the
video. Then it takes a snapshot of the video and returns it to the called function.
d. CreateDatabase.m – The eigenface algorithm requires that we create one-
dimensional array from the database of images. This function does this task.
e. Eigenface.m – Eigenface algorithm requires various characteristics of the face
images in order for efficient recognition which are the mean, average and the
eigenfaces. This function performs all these functions.
f. Recognition.m – The detected faces are compared with the parameters from the
training database and gives the name of the image, which is a number to which it
matches satisfactorily.
g. Record_attendance.m – Based on the number obtained from the recognition
module, we determine to which student the image belongs using a switch case and
update the field in the excel sheet.
h. Delete_test.m – Finally the images captures during the process has to be deleted to
avoid unnecessary wastage of the memory. This function does the same.
34. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 27
Chapter 7
RESULTS AND ANALYSIS
Using the all the functions we have created, we have tested for output in using existing test
images as well as in real-time. Following section, the screenshots of the output of different
functions are given. We have tested the system with the help of four volunteers.
7.1 Collect Training Dataset
Using the function TrainDatabase we create a database of the enrolled students which is
stored in the folder.
Figure 7-1 Collected set of training images
With the help of four volunteers, three images of each candidate is stored on the database
as shown in the figure 5-1. For more accuracy we can increase the number of training
images but with a compromise in the speed of calculation. However, for our application
calculation speed variation won’t be problem since a class timing is typically at least one
hour and this period is just a lot more than the computation time takes by the algorithm.
One thing we have keep in mind during this phase is to take the picture in ambient lighting
and the frontal face must be clearly visible. Also there must be slight variation on the
position or expression of the student in each captured image for better results.
35. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 28
7.2 Image capturing, Face Detection and Cropping
An appropriate image of the classroom is taken with. The camera has to be so placed such
that the faces of all the students are clearly visible.
Figure 7-2 Capturing of the classroom image and face detection
In normal lighting conditions and based on the proper sitting posture of the students the
faces are efficiently captured. The classroom lighting has to be efficiently maintained. Also
in case of blackouts appropriate alternatives have to be arranged.
All the detected faces which can be seen in figure 7-2, are cropped and saved in the Test
Database folder. From this location the next algorithm read the image and further
processing are carried out. The path of the folder must be exactly specified. Also the name
each of the faces are given as numbers automatically. This helps in easier reading of the
images from the folder.
7.3 Face Recognition
Cropped facial images are fed into the face recognition algorithm and we get the results.
The Eigen faces algorithm is applied to the image and compared with the database. We get
the output as in figure 7-3 after this process.
36. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 29
Figure 7-3 A student is recognized and appropriate message is displayed
If a person whose database is not present in the database, his image is simply ignored.
However proper lighting has to be maintained in order to prevent in any false detection.
7.4 Output in MS Excel
We get the output as given below. After that we can derive the results in appropriate format
using different function in the spreadsheet as in figure 7-4. We can get the following
parameters by using this format as output as shown in the figure. This function is
performed using the Spreadsheet Link Ex toolbox of the MATLAB.
• If a person is present, a ‘1’ is passed on to the particular field of the student
• The date and time is also passed on to the sheet.
We can include any number of students’ data using this system and provided we use a better
quality of an image capturing device.
In the next section we describe how we integrate all these function y using the Graphical
User Interface (GUI). This gives an easy to use interface to the users.
37. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 30
Figure 7-4 Output obtained in the excel format (.xlsx)
7.2 Graphical user interface
After the coding we have created a GUI for an easy-to-use interface as running the code
from MATLAB has no charm. We have included the GUI for taking attendance as well as
for collecting the training images as shown in figure 7-5.
Figure 7-5 Graphical User Interface for the system
38. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 31
The following figures includes the snapshots of the GUI.
In the GUI we have included the following feature-
• Push button for capturing training images
• Axes which displays the captured image, detected face and the cropped image
• Push button for attendance system
• Axes showing the streaming video of the classroom
The working of the GUI is shown in the following screenshots -
Figure 7-6 Capturing training images using the GUI
Figure 7-7 Recording attendance using the GUI
39. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 32
Chapter 8
ADVANTAGES AND APPLICATIONS
1. Maintains Overall Records: An automated face recognition attendance system
maintains the overall presence record of the students in the institution. Leaves taken
by the students, date of absent each data is stored in the system.
2. Get Rid of Pen & Paper System: The newest technology helps in replacing the older
paper register method efficiently. It also saves money that the organization uses to
spend on the paper. Face-recognition time attendance system gives better maintenance
of data as it supports the electronic medium of data storage. Also the system gives a
good impression about the organization in front of the business clients and other
concerned people.
3. Financial Benefits: The face-recognition time attendance system helps in saving time,
eliminates the manual mistakes and controls the overall system. Since the face
recognition system controls every single event electronically therefore, reduces the
possibility of error. The attendance is noted down electronically therefore it saves time
of the lecturers which they can use efficiently in lecturing.
4. Easy Integration: Integrated Biometric facial systems are also easy to program into
any computer system. Usually they will work with existing software that one has in
their place.
5. High Success Rate: Facial biometrics technology today has a high success rate,
especially with the emergence of 3d face recognition technologies. It is extremely
difficult to fool the system, so one can feel secure about the system.
6. Proxy attendance is eliminated: Attendance is taken automatically by the camera
placed in the classroom therefore there will be no chances of proxy attendances.
7. Saves Time: In traditional attendance marking system Lecturer calls each student’s
name with respect to their ids which is a very much time consuming job this system
restores the time consumed for calling attendance by automatically marking
attendance.
8. Less Mistakes: here will be chances of making mistakes while manually marking
attendances by lecturers, while taking attendance automatically there will not be any
chances of mistakes since the system is computer based.
9. Virtual Classroom: Virtual classrooms are the class rooms without the lecturers to
teach as students will be learning online. This system is very useful in virtual
40. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 33
classrooms where there will be no lecturers to take attendances this system will
automatically manage the attendances of the students.
10. Simple Algorithm & Flowcharts: This system uses a simple algorithm and flowchart
which is easy to understand as there are no complicated sections, information flow is
simple as there is less hardware’s components used therefore each section is clearly
understood.
We see the system have lot of advantages of the system. But as in most systems some
drawbacks have been observed in the system.
• Sensitive to Light – If the ambient lighting in the training images and the images
taken during the processing varies, there is a high possibility in face recognition
incorrectly. Hence we need to keep in mind the lighting conditions of the classroom
during the process of collecting the database of the students.
41. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 34
Chapter 9
FUTURE SCOPE
The system we have developed has successfully, able to accomplish the task of marking
the attendance in the classroom automatically and output is obtained in an excel sheet
as desired in real-time. However, in order to develop a dedicated system which can be
implemented in an educational institution, a very efficient algorithm which is
insensitive to the lighting conditions of the classroom has to be developed. Also a
camera of the optimum resolution has to be utilised in the system. Another important
aspect where we can work towards is creating an online database of the attendance and
automatic updating of the attendance into it keeping in mind the growing popularity of
Internet of Things. This can be done by creating a standalone module which can be
installed in the classroom having access to internet, preferably a wireless system. These
developments can greatly improve the applications of the project.
42. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 35
Chapter 10
CONCLUSION
In this system we have implemented an attendance system for a lecture, section or
laboratory by which lecturer or teaching assistant can record students’ attendance. It saves
time and effort, especially if it is a lecture with huge number of students. Automated
Attendance System has been envisioned for the purpose of reducing the drawbacks in the
traditional (manual) system. This attendance system demonstrates the use of image
processing techniques in classroom. This system can not only merely help in the attendance
system, but also improve the goodwill of an institution.
43. Automated Attendance System based on Facial Recognition
Department of ECE, SMVITM, Bantakal Page 36
REFERENCES
[1] M. T. a. A. Pentland, "Eigenfaces For Recognition," Journal of Cognitive
Neuroscience, vol. 3, no. 1, 1991.
[2] A. V. a. R. Tokas, "Fast Face Recognition Using Eigen Faces," IJRITCC, vol. 2, no.
11, pp. 3615-3618, November 2014.
[3] Paul Viola and Michael J. Jones, "Robust Real-Time Face Detection," International
Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, May 2004.
[4] N. J. M. M. K. a. H. A. Mayank Agarwal, "Face Recognition Using Eigenface
aproach," IRCSE, vol. 2, no. 4, pp. 1793-8201, August 2010.
[5] Vinay Hermath, Ashwini Mayakar, "Face Recognition Using Eigen Faces and,"
IACSIT, vol. 2, no. 4, pp. 1793-8201, August 2010.
44. Personal Profile
Mr. Chethan R
Project Guide
Mr. Chethan R received his B.E. degree in E&C Engineering
from SRSIT, Bangalore in the year 2008 and M.Tech. in
Electricals and Electronics from NMAM Institute of
Technology, Nitte, India in the year 2012.
He is an Assistant Professor of E&C Engineering at SMVITM
since 2011. His areas of interest include VLSI design, Embedded
systems and Bio-medical Electronics. His papers have been
published in international journals and has presented them in
international conference.
Student’s Name: Rakshitha
USN: 4MW12EC059
Address: No.7 Mandavi Plaza, Udupi
Student’s Name: S R Dhanush
USN: 4MW12EC065
Address: Yakshagana Kendra, Indrali, Udupi, India
Student’s Name: Shreeraksha Shetty
USN: 4MW12EC075
Address: Asha Nilaya, Nellikatte Hirebettu Athradi
Udupi, India