INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
This document summarizes a seminar presentation on face recognition technology. It begins with an introduction to facial recognition systems and what biometrics are. It then discusses why facial recognition is chosen over other biometrics, the differences between facial recognition and face detection, and how facial recognition systems work. Application areas are identified, such as security, government ID, casinos. Advantages include convenience and cost-effectiveness, while disadvantages include issues with lighting, pose, and privacy concerns. The growth rate of the facial recognition market is projected to be nearly 14% annually through 2022.
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works, its implementation which involves image acquisition, processing, distinctive characteristic location and template matching. It also outlines the strengths and weaknesses of facial recognition as well as its applications in areas like border control, computer security, and banking. While facial recognition provides advantages like convenience and easy use, it also has disadvantages such as being impacted by changes in user appearance.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
This document discusses face recognition systems and the use of artificial neural networks for face recognition. It describes the basic steps in a face recognition system as face detection, alignment, feature extraction, and matching. Two types of neural networks that can be used for recognition are described - Radial Basis Function Networks and Back Propagation Networks. RBF Networks have an input, hidden, and output layer while BPN uses backpropagation of errors to adjust weights. The document also outlines some applications of face recognition systems such as ID verification and criminal investigations.
This document provides an overview of face recognition technology. It discusses 2D and 3D facial recognition, how the technology works by measuring facial features to create a unique face print, hardware and software requirements, advantages like identifying repeat offenders, and applications in security, multimedia, and law enforcement. The conclusion states that while progress has been made, continued work is needed to develop more accurate systems.
1. The document discusses face recognition using an eigenface approach, which uses principal component analysis to extract features from a database of faces to generate eigenfaces that can be used to identify unknown faces.
2. The eigenface approach takes into account the entire face for recognition and is relatively insensitive to small changes in faces. It is faster, simpler, and has better learning capabilities compared to other approaches.
3. Some limitations are that accuracy is affected if lighting and face position vary greatly, it only works with grayscale images, and noisy or partially occluded faces decrease recognition performance.
Sandeep Sharma presented on face recognition. He discussed the history and types of face recognition including 2D and 3D. He explained how face recognition works by measuring facial landmarks and using algorithms like PCA and LDA to analyze features. Challenges included disguises and large crowds. Future uses could include law enforcement, banking security, and airports. Advancements are still needed for widescale deployment.
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
This document summarizes a seminar presentation on face recognition technology. It begins with an introduction to facial recognition systems and what biometrics are. It then discusses why facial recognition is chosen over other biometrics, the differences between facial recognition and face detection, and how facial recognition systems work. Application areas are identified, such as security, government ID, casinos. Advantages include convenience and cost-effectiveness, while disadvantages include issues with lighting, pose, and privacy concerns. The growth rate of the facial recognition market is projected to be nearly 14% annually through 2022.
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works, its implementation which involves image acquisition, processing, distinctive characteristic location and template matching. It also outlines the strengths and weaknesses of facial recognition as well as its applications in areas like border control, computer security, and banking. While facial recognition provides advantages like convenience and easy use, it also has disadvantages such as being impacted by changes in user appearance.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
This document discusses face recognition systems and the use of artificial neural networks for face recognition. It describes the basic steps in a face recognition system as face detection, alignment, feature extraction, and matching. Two types of neural networks that can be used for recognition are described - Radial Basis Function Networks and Back Propagation Networks. RBF Networks have an input, hidden, and output layer while BPN uses backpropagation of errors to adjust weights. The document also outlines some applications of face recognition systems such as ID verification and criminal investigations.
This document provides an overview of face recognition technology. It discusses 2D and 3D facial recognition, how the technology works by measuring facial features to create a unique face print, hardware and software requirements, advantages like identifying repeat offenders, and applications in security, multimedia, and law enforcement. The conclusion states that while progress has been made, continued work is needed to develop more accurate systems.
1. The document discusses face recognition using an eigenface approach, which uses principal component analysis to extract features from a database of faces to generate eigenfaces that can be used to identify unknown faces.
2. The eigenface approach takes into account the entire face for recognition and is relatively insensitive to small changes in faces. It is faster, simpler, and has better learning capabilities compared to other approaches.
3. Some limitations are that accuracy is affected if lighting and face position vary greatly, it only works with grayscale images, and noisy or partially occluded faces decrease recognition performance.
Sandeep Sharma presented on face recognition. He discussed the history and types of face recognition including 2D and 3D. He explained how face recognition works by measuring facial landmarks and using algorithms like PCA and LDA to analyze features. Challenges included disguises and large crowds. Future uses could include law enforcement, banking security, and airports. Advancements are still needed for widescale deployment.
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
Face recognition technology uses digital images and video frames to automatically identify or verify a person. It works by comparing selected facial features from an image to a facial database containing 80 landmarks on each face, such as distance between eyes, width of nose, and jaw lines. This is done using local feature analysis algorithms to encode faces and create unique numerical codes, or "face prints", that can be matched against large databases. While face recognition provides convenience over other biometrics like fingerprints, it has disadvantages such as an inability to distinguish identical twins and potential issues with database searching speeds. However, decreasing costs are leading to more widespread deployment of this technology in applications like access control, advertising, and retail point-of-sale systems.
A facial recognition system uses computer applications to identify or verify a person from images or video by comparing facial features to a database. It can be used for security systems and is similar to other biometrics like fingerprints. Some key parts of faces used for comparison include the distance between the eyes, width of the nose, and structure of cheek bones. Algorithms continue improving to account for challenges like changes in lighting or facial expressions. Facial recognition has various applications and is expected to become more widespread and integrated into security and social networks in the future.
The document discusses face recognition technology as a biometric authentication method. It describes how face recognition works by detecting nodal points on faces and creating unique faceprints. The advantages are that face recognition is convenient, socially acceptable and inexpensive compared to other biometrics. However, face recognition has difficulties with identical twins and environmental/appearance changes reducing accuracy over time. The document also outlines applications in security, law enforcement, banking, and commercial access control.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
This application was design with help of OpenCv and C#.
Facial recognition (or face recognition) is a type of bio-metric application that can identify a specific individual in a digital image by analysing and comparing patterns.
Face recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If we look at the mirror, we can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features.
This application take picture of your face and after storing it.
Then it start identifying all face which are store in database.
Face recognition technology may help solve problems with identity verification by analyzing facial features instead of passwords or pins. The document outlines the key stages of face recognition systems including data acquisition, input processing, and image classification. It also discusses advantages like convenience and ease of use, as well as limitations such as an inability to distinguish identical twins. Potential applications are identified in government, security, and commercial sectors.
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
This document provides an overview of facial recognition technology. It discusses how facial recognition works by measuring distinguishing features of the face. It covers 2D and 3D facial recognition, with 3D providing more accuracy. The document outlines the basic steps 3D facial recognition systems use to verify identity, including acquiring an image, determining head position/size, measuring facial curves to create a template, and matching templates. It also discusses the technology's advantages in identification, current and future applications, and some disadvantages regarding variations in poses, lighting and privacy issues.
Automatic Attendance system using Facial RecognitionNikyaa7
It is a boimetric based App,which is gradually evolving in the universal boimetric solution with a virtually zero effort from the user end when compared with other boimetric options.
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.
Face recognition technology uses unique facial features to identify or verify individuals. It works by measuring distances between nodal points on the face, like the eyes, nose, and chin. The technology has various applications and advantages over other biometrics like fingerprints. It does not require physical contact and can identify people quickly without an expert. While very accurate, face recognition may have issues distinguishing between identical twins. The document discusses the components, implementation, advantages and uses of face recognition systems.
Facial recognition systems use computer applications to identify or verify people from images or video by comparing facial features to a database. They analyze over 80 nodal points on faces, such as eye distance and nose width. 3D modeling provides more accuracy by measuring curves and creating unique templates to match against databases. While useful for security and IDs, facial recognition raises privacy issues if misused due to its ability to identify people without consent.
Face Detection Attendance System By Arjun SharmaArjun Agnihotri
This document proposes a facial recognition-based attendance system for classrooms. It works by detecting faces in the classroom and comparing them to a database of student faces to take attendance automatically. The system consists of image processing and comparison modules to recognize faces, extract features, compare templates, and determine a match or non-match. It operates by detecting 80 nodal points on faces to create unique faceprints for identification and verification. While this system can automate attendance tracking without paper, facial recognition technology still has limitations around accuracy and environmental factors.
Deep learning on face recognition (use case, development and risk)Herman Kurnadi
1) Face recognition using deep learning methods has achieved high accuracy, nearing and sometimes surpassing human-level performance on some datasets.
2) The document outlines the key steps in face recognition systems using deep learning: face detection, alignment, feature extraction, and recognition. It discusses several influential deep learning models that have improved accuracy.
3) Applications discussed include security, health, and marketing/retail uses. Concerns about bias and privacy are also mentioned.
This document summarizes a student presentation on a face recognition lecture attendance system. The system uses image processing and comparison to recognize students' faces from a high-definition camera feed and compare them to a database to take attendance. It is controlled by the faculty member, who instructs the system to start and end recording. The system is intended to smartly track that students remain for the entire lecture session and also function as surveillance. At the end, it reverts a full attendance report back to a central database. Diagrams including class, activity, sequence and use case diagrams are also presented to depict the system workflow and actors.
Facial recognition is a type of biometric system that identifies individuals by analyzing patterns in images of their faces. The presentation summarizes how facial recognition systems work by detecting faces, normalizing them, extracting distinguishing features to create a template, and then matching templates to identify individuals. It notes advantages like convenience but also challenges like difficulty with changes in appearance over time. Applications discussed include security, banking, and voter verification.
Object detection is a computer vision technique that identifies objects in images and videos. It can detect things like faces, humans, buildings, and cars. Object detection has applications in areas like image retrieval, video surveillance, and face detection. Image processing techniques are used to both improve images for human interpretation and to make images more suitable for machine perception. These techniques include enhancing edges, converting images to binary, greyscale, or true color formats. Face detection is a common application that finds faces in images and ignores other objects. It is often used as the first step in face recognition systems.
Face recognition is a biometric technique that uses unique facial measurements to identify or verify individuals in images. It analyzes the shape, pattern, and positioning of facial features. Face recognition systems first detect faces in images, then extract distinguishing nodal points like eye depth, nose width, and distance between eyes. They compare these measurements to templates stored in a database to identify matches. While convenient and non-invasive, face recognition has limitations like inability to distinguish identical twins and decreased accuracy with changes in appearance. It finds applications in security, law enforcement, and commercial uses like building access control and ATMs.
Automatic Attendance System using Deep LearningSunil Aryal
This document presents an automatic attendance system using deep learning for facial recognition. It begins with an introduction that explains how the system uses real-time face recognition algorithms integrated with a university management system to automate attendance tracking without manual input. The methodology section then outlines the 5 main steps: 1) taking pictures with a high definition camera, 2) detecting faces, 3) recognizing faces, 4) processing the database, and 5) marking attendance. It describes using CNN and MTCNN models for face detection and a ResNet-34 architecture trained on a large dataset for face recognition, achieving 97% accuracy. The conclusion states this system provides an accurate, transparent, and time-efficient way to take attendance without human bias or manual work.
The document summarizes an OpenCV based image processing attendance system. It discusses using OpenCV to detect faces in images and recognize faces by comparing features to a database. The key steps are face detection using Viola-Jones detection, face recognition using eigenfaces generated by principal component analysis to project faces into "face space", and measuring similarity by distance between projections.
Face recognition technology uses digital images and video frames to automatically identify or verify a person. It works by comparing selected facial features from an image to a facial database containing 80 landmarks on each face, such as distance between eyes, width of nose, and jaw lines. This is done using local feature analysis algorithms to encode faces and create unique numerical codes, or "face prints", that can be matched against large databases. While face recognition provides convenience over other biometrics like fingerprints, it has disadvantages such as an inability to distinguish identical twins and potential issues with database searching speeds. However, decreasing costs are leading to more widespread deployment of this technology in applications like access control, advertising, and retail point-of-sale systems.
A facial recognition system uses computer applications to identify or verify a person from images or video by comparing facial features to a database. It can be used for security systems and is similar to other biometrics like fingerprints. Some key parts of faces used for comparison include the distance between the eyes, width of the nose, and structure of cheek bones. Algorithms continue improving to account for challenges like changes in lighting or facial expressions. Facial recognition has various applications and is expected to become more widespread and integrated into security and social networks in the future.
The document discusses face recognition technology as a biometric authentication method. It describes how face recognition works by detecting nodal points on faces and creating unique faceprints. The advantages are that face recognition is convenient, socially acceptable and inexpensive compared to other biometrics. However, face recognition has difficulties with identical twins and environmental/appearance changes reducing accuracy over time. The document also outlines applications in security, law enforcement, banking, and commercial access control.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
This application was design with help of OpenCv and C#.
Facial recognition (or face recognition) is a type of bio-metric application that can identify a specific individual in a digital image by analysing and comparing patterns.
Face recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If we look at the mirror, we can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features.
This application take picture of your face and after storing it.
Then it start identifying all face which are store in database.
Face recognition technology may help solve problems with identity verification by analyzing facial features instead of passwords or pins. The document outlines the key stages of face recognition systems including data acquisition, input processing, and image classification. It also discusses advantages like convenience and ease of use, as well as limitations such as an inability to distinguish identical twins. Potential applications are identified in government, security, and commercial sectors.
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
This document provides an overview of facial recognition technology. It discusses how facial recognition works by measuring distinguishing features of the face. It covers 2D and 3D facial recognition, with 3D providing more accuracy. The document outlines the basic steps 3D facial recognition systems use to verify identity, including acquiring an image, determining head position/size, measuring facial curves to create a template, and matching templates. It also discusses the technology's advantages in identification, current and future applications, and some disadvantages regarding variations in poses, lighting and privacy issues.
Automatic Attendance system using Facial RecognitionNikyaa7
It is a boimetric based App,which is gradually evolving in the universal boimetric solution with a virtually zero effort from the user end when compared with other boimetric options.
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.
Face recognition technology uses unique facial features to identify or verify individuals. It works by measuring distances between nodal points on the face, like the eyes, nose, and chin. The technology has various applications and advantages over other biometrics like fingerprints. It does not require physical contact and can identify people quickly without an expert. While very accurate, face recognition may have issues distinguishing between identical twins. The document discusses the components, implementation, advantages and uses of face recognition systems.
Facial recognition systems use computer applications to identify or verify people from images or video by comparing facial features to a database. They analyze over 80 nodal points on faces, such as eye distance and nose width. 3D modeling provides more accuracy by measuring curves and creating unique templates to match against databases. While useful for security and IDs, facial recognition raises privacy issues if misused due to its ability to identify people without consent.
Face Detection Attendance System By Arjun SharmaArjun Agnihotri
This document proposes a facial recognition-based attendance system for classrooms. It works by detecting faces in the classroom and comparing them to a database of student faces to take attendance automatically. The system consists of image processing and comparison modules to recognize faces, extract features, compare templates, and determine a match or non-match. It operates by detecting 80 nodal points on faces to create unique faceprints for identification and verification. While this system can automate attendance tracking without paper, facial recognition technology still has limitations around accuracy and environmental factors.
Deep learning on face recognition (use case, development and risk)Herman Kurnadi
1) Face recognition using deep learning methods has achieved high accuracy, nearing and sometimes surpassing human-level performance on some datasets.
2) The document outlines the key steps in face recognition systems using deep learning: face detection, alignment, feature extraction, and recognition. It discusses several influential deep learning models that have improved accuracy.
3) Applications discussed include security, health, and marketing/retail uses. Concerns about bias and privacy are also mentioned.
This document summarizes a student presentation on a face recognition lecture attendance system. The system uses image processing and comparison to recognize students' faces from a high-definition camera feed and compare them to a database to take attendance. It is controlled by the faculty member, who instructs the system to start and end recording. The system is intended to smartly track that students remain for the entire lecture session and also function as surveillance. At the end, it reverts a full attendance report back to a central database. Diagrams including class, activity, sequence and use case diagrams are also presented to depict the system workflow and actors.
Facial recognition is a type of biometric system that identifies individuals by analyzing patterns in images of their faces. The presentation summarizes how facial recognition systems work by detecting faces, normalizing them, extracting distinguishing features to create a template, and then matching templates to identify individuals. It notes advantages like convenience but also challenges like difficulty with changes in appearance over time. Applications discussed include security, banking, and voter verification.
Object detection is a computer vision technique that identifies objects in images and videos. It can detect things like faces, humans, buildings, and cars. Object detection has applications in areas like image retrieval, video surveillance, and face detection. Image processing techniques are used to both improve images for human interpretation and to make images more suitable for machine perception. These techniques include enhancing edges, converting images to binary, greyscale, or true color formats. Face detection is a common application that finds faces in images and ignores other objects. It is often used as the first step in face recognition systems.
Face recognition is a biometric technique that uses unique facial measurements to identify or verify individuals in images. It analyzes the shape, pattern, and positioning of facial features. Face recognition systems first detect faces in images, then extract distinguishing nodal points like eye depth, nose width, and distance between eyes. They compare these measurements to templates stored in a database to identify matches. While convenient and non-invasive, face recognition has limitations like inability to distinguish identical twins and decreased accuracy with changes in appearance. It finds applications in security, law enforcement, and commercial uses like building access control and ATMs.
Automatic Attendance System using Deep LearningSunil Aryal
This document presents an automatic attendance system using deep learning for facial recognition. It begins with an introduction that explains how the system uses real-time face recognition algorithms integrated with a university management system to automate attendance tracking without manual input. The methodology section then outlines the 5 main steps: 1) taking pictures with a high definition camera, 2) detecting faces, 3) recognizing faces, 4) processing the database, and 5) marking attendance. It describes using CNN and MTCNN models for face detection and a ResNet-34 architecture trained on a large dataset for face recognition, achieving 97% accuracy. The conclusion states this system provides an accurate, transparent, and time-efficient way to take attendance without human bias or manual work.
The document summarizes an OpenCV based image processing attendance system. It discusses using OpenCV to detect faces in images and recognize faces by comparing features to a database. The key steps are face detection using Viola-Jones detection, face recognition using eigenfaces generated by principal component analysis to project faces into "face space", and measuring similarity by distance between projections.
The document discusses biometric authentication and gait recognition. It defines biometric authentication as using physiological or behavioral characteristics to identify individuals. Gait recognition specifically aims to identify people based on how their silhouette changes over time as they walk. While unobtrusive, gait recognition is subject to effects of clothing and viewing angles. The document outlines different biometric devices and modalities like fingerprints, facial recognition, and speech recognition. It also describes how gait recognition extracts 1D signals from 2D silhouettes to analyze an individual's walking pattern for identification.
This document discusses palmprint recognition as a biometric for user identification. It describes the architecture of the palm including key creases. It then outlines the design of a typical palmprint recognition system which involves scanning, preprocessing, feature extraction, matching, and a database. Various scanning and matching algorithms are evaluated in terms of cost, speed, accuracy and security. Fusion with other biometrics can improve performance and privacy is protected through security steps and identification rather than verification.
1. The document discusses gait recognition from video for biometric identification. It provides background on biometric recognition and discusses gait as an identifying biometric trait that can be captured from a distance.
2. Various research approaches to gait recognition are covered, including model-based, motion-based, and mixed approaches. Commonly used gait recognition databases are also listed.
3. Recent works applying techniques like matrix representations, Bayesian frameworks, and symmetry-based detection are summarized, demonstrating applications in human identification, activity recognition, and scene registration. Future directions discussed include improving performance under more natural conditions.
Automated attendance system based on facial recognitionDhanush Kasargod
A MATLAB based system to take attendance in a classroom automatically using a camera. This project was carried out as a final year project in our Electronics and Communications Engineering course. The entire MATLAB code I've uploaded it in mathworks.com. Also the entire report will be available at academia.edu page. Will be delighted to hear from you.
Spatial domain image enhancement techniques operate directly on pixel values. Some common techniques include point processing using gray level transformations, mask processing using filters, and histogram processing. Histogram equalization aims to create a uniform distribution of pixel values by mapping the original histogram to a wider range. This improves contrast by distributing pixels more evenly across gray levels.
This document discusses face recognition technology. It begins by defining facial recognition as a type of biometric software that can identify individuals by analyzing patterns in digital images. It then discusses the components and process of how face recognition systems work, including capturing images, extracting nodal point data to create a face print, storing prints in a database, and matching new images to those in the database. The document also covers performance metrics, software, applications, advantages and disadvantages, and concludes that face recognition technology is becoming more cost effective and accurate for various commercial and security uses.
This document discusses face recognition technology. It begins by defining facial recognition as a type of biometric software that can identify individuals by analyzing patterns in digital images. It then discusses the components and process of how face recognition systems work, including capturing images, extracting nodal point data to create a face print, storing prints in a database, and matching new images to those in the database. The document also covers performance metrics, software, applications, advantages and disadvantages, and concludes that face recognition technology is becoming more cost effective and accurate for various commercial and security uses.
This seminar presentation provides an overview of face recognition technology. It discusses how face recognition works by measuring nodal points on the face and creating a numerical face print. The key advantages are that face recognition is convenient, easy to use, and inexpensive compared to other biometrics. However, it cannot distinguish identical twins. The presentation outlines common applications in government (e.g. law enforcement, security), commercial (e.g. banking, access control), and concludes that costs are decreasing which will lead to more widespread deployment of face recognition technologies.
Face recognition is a type of biometric software that uses analysis of facial patterns to identify individuals. It has various applications including security, law enforcement, and social media photo tagging. The technology works by measuring nodal points on faces like eye and nose position to create unique numerical faceprints for identification and verification. While effective, face recognition depends on clear images and has limitations with expressions, lighting, or obscured faces. It is increasingly being implemented in areas like access control, immigration, and banking due to lower costs.
This document discusses facial recognition technology. It begins with an introduction to biometrics and the need for facial recognition. It then describes the process of facial recognition, including data capture, extraction of features, comparison, and matching. The key components of a facial recognition system and how it works are also outlined. Advantages include convenience and ease of use, while disadvantages relate to issues with lighting, pose, and privacy concerns. The document concludes by describing applications of facial recognition technology in government, security, banking, and other commercial sectors.
This document summarizes a seminar report on face recognition technology. It discusses how biometrics can be used to recognize individuals, with physiological biometrics including facial recognition. Face recognition uses computer vision to identify or verify a person's identity from their face. The document then outlines the four stages all identification technologies use - capture, extraction, comparison, and match or non-match. It provides details on how face recognition systems work, the software used, advantages like convenience, and applications in government, commercial, and other sectors.
Attendance system based on face recognition using python by Raihan Sikdarraihansikdar
The document discusses face recognition technology for use in an automatic attendance system. It first defines biometrics and face recognition, explaining that face recognition identifies individuals using facial features. It then covers how face recognition systems work by detecting nodal points on faces to create unique face prints. The document proposes using such a system to take student attendance in online classes during the pandemic, noting advantages like ease of use, increased security, and cost effectiveness. It provides examples of how the system would capture images, analyze features, and recognize enrolled students to record attendance automatically.
seminar presentation on Face ricognition technologyJawhar Ali
This document discusses face recognition technology, which uses computer vision to identify or verify a person's identity based on their face. It describes how face recognition systems work by analyzing nodal points on the face and comparing new images to existing data using techniques like detection, alignment, normalization, and matching. The document also outlines some advantages and disadvantages of this biometric technology, and discusses potential applications in areas like law enforcement, security, banking, and more.
1. The document discusses using facial recognition technology for ATM security to prevent unauthorized access through stolen cards or PINs. It analyzes existing facial recognition methods like eigenfaces and proposes using 3D recognition to address spoofing issues.
2. The methodology section outlines the steps - locating an open source facial recognition program using local feature analysis, extracting features from faces, and searching databases to find matches.
3. Results show that Bank United was the first to use iris recognition at ATMs for a cardless, password-free way to withdraw money. The conclusion is that facial recognition is highly secure and widely used in security applications due to technological advances in identification and verification.
This case study examines face recognition technology for e-attendance. It discusses the history of face recognition, defines biometrics, and explains why face recognition was chosen over other biometrics. The key components of face recognition systems are described, including how the systems work by detecting, aligning, normalizing, and matching facial features. Current and potential future applications of the technology are also outlined.
The document discusses face detection technology, including its history from the 1960s, key advances like the Viola-Jones algorithm in 2001, and both its growing capabilities and remaining challenges. Face detection is now fast, automatic, and can identify multiple faces, but still struggles with angle variation. It has many applications in security, attendance tracking, and photography but requires further algorithm improvements to achieve full accuracy.
The document discusses biometrics, which is the study of methods for uniquely recognizing humans based on physical and behavioral traits. Some examples of physiological biometrics are fingerprint, face recognition, DNA, hand and palm geometry, and iris recognition. Behavioral biometrics include typing rhythm, gait, and voice. The document then explains the process of biometric systems which involves capturing biometric data, creating a template, storing it in a database, and comparing new captures against stored templates to authenticate users. It discusses some challenges with biometric technologies including privacy issues, discrimination concerns, and the permanence of biometrics.
Face recognition is a computer application that automatically identifies or verifies an individual from a digital image or video footage. It works by detecting faces, aligning and normalizing them, representing the facial features with unique codes, and then matching new images to stored facial data. It has various applications including commercial uses like daycare sign-in/out systems, residential security cameras, and banking ATMs. While fast and convenient, face recognition has disadvantages like being impacted by changes in appearance and an inability to distinguish identical twins.
Face recognition is a type of biometric system that uses analysis and comparison of facial patterns to identify individuals from digital images. It works by detecting distinct nodal points on the face and measuring relationships between features like eye separation, nose width, and jaw line. The process involves image acquisition, processing, locating distinguishing characteristics, and template matching. Some advantages are its ability to identify from photos and operate without cooperation, while weaknesses include reduced accuracy from environmental changes or aging. Applications include security, child pickup verification, and banking authentication.
Its a power point presentation on face recognition system . In the covid time biometrics is not a good option thats why we need a face recognition system
Face recognition technology provides a solution for fast and accurate user identification and authentication by verifying a person's identity based on their face. It works by detecting facial features and measuring the distances between nodal points like the eyes, nose, and jawline to create a unique facial signature or "faceprint". The system then compares new facial images to those stored in a database to match faces or verify identities. While face recognition has advantages like convenience and low cost, it also has limitations such as an inability to distinguish identical twins. It finds applications in security systems, law enforcement, immigration, and banking.
Biometric technology is a unique, measurement characteristic of human body like face and voice recognition. It is providing strong security for your personal information.
IRJET-Human Face Detection and Identification using Deep Metric LearningIRJET Journal
This document discusses a project that uses deep metric learning techniques for human face detection and identification in images and videos. Deep metric learning outputs a real-valued vector rather than a single classification. It uses libraries like OpenCV, Dlib, scikit-learn and Keras to build neural networks for facial recognition. The goals are to develop a system that can identify faces even from low quality images with variations in illumination, expression, angle and occlusions. Existing face recognition has challenges in these conditions, so the aim is to improve accuracy rates for normal and non-ideal images through deep metric learning approaches.
Attendance System using Face RecognitionIRJET Journal
This document describes an automated attendance system using face recognition. It discusses using algorithms like Viola-Jones for face detection and PCA for feature extraction and SVM for classification. The system works by capturing images of students' faces with a camera as they enter the classroom. It then detects faces, recognizes the students, and automatically marks their attendance on an attendance sheet. The system is presented as an improvement over previous biometric-based attendance systems in that it is faster, more convenient, and helps monitor students.
This document discusses the components, classification, properties, workability, and strength testing of concrete. Concrete is made up of cement, coarse aggregate, fine aggregate, air, and water. It can be classified as hardened or fresh concrete. The properties of fresh concrete include workability, segregation, and bleeding, while hardened concrete properties include strength, impermeability, durability, and dimensional variations. Workability is tested using slump, compaction factor, and Vebe tests. Compressive strength of hardened concrete is tested using cube or cylinder tests.
Hello readers,
In this presentation, I am sharing Fiber Reinforced Concrete.
The following parameters are discussed in the presentation:
History.
Why Fibers are used?
Type of fibers.
Mechanical properties of FRC.
Factors affecting properties of FRC.
Advantages and Disadvantages of FRC.
Applications of FRC.
Hello readers,
In this presentation, I am sharing Maintenance strategies & case studies.
The following parameters are discussed in the presentation:
Inspections, Estimation of deterioration levels and rates, Evaluation of performance of the structure, Remedial actions & Recordings.
Hello guys,
I am here to share my knowledge on Non-Destructive Testing (NDT). In this presentation, I tried to explain on the following topics:
1. why we use NDT (purpose)?
2. Condition Assessment of the Structures by Non-destructive Evaluation Techniques
3. Concrete Petrography Implications
4. Potential as a DiagnosticTechnique
5. Common Applications of Microscopy in Concrete Science and Technology
6. Thermographic (Infrared Thermal Imaging)
With interesting pictures
and much more...
I put some effort to present in front of you. So, kindly if you find any mistakes feel free to text.
Hello, I am here to present a case study on SHANGHAI TOWER.
The following contents are discussed in this presentation:
1. INTRODUCTION i.e basics about SHANGHAI TOWER.
2. ARCHITECTURAL SALIENT FEATURES
3. STRUCTURAL SYSTEM, here different types of structural systems are explained.they are
a) Core Wall Inner Tube System
b) Outer Mega Frame System
c) Super column system.
d) Floor System
e) Foundation System adopted for the Tower
Hello readers,
In this presentation, i am sharing Concept & Construction of Palm Island.
The following parameters are discussed in the presentation:
Where?
Why?
When?
Construction Machinery
Construction Steps
Conclusion.
COMPUTER VISION SYNDROME( CVS ) with SOFTWARE LINK involved in it.JASHU JASWANTH
In this ppt , you can learn how to reduce the computer eye strain. and the topics i covered was
INTRODUCTION
SYMPTOMS
CAUSES
CONVENTIONAL TREATMENT
SELF HELP FOR COMPUTER EYE STRAIN
SOFTWARE
and most importantly you can see the software which is more helpful to reduce eye strain.
and you can see the link to download , then add interface of the website.
In this upload, you can refer all the topics related to the women empowerment.
Definition of women empowerment
various principles in women empowerment
Necessity of women empowerment
The process of women empowerment
NATIONAL POLICY FOR THE EMPOWERMENT OF WOMEN (2001)
Laws Related to Women
Necessity of women reservation:
A SIMPLE QUOTATION ON WOMEN EMPOWERMENT
detailed NATIONAL POLICY FOR THE EMPOWERMENT OF WOMEN (2001) is mentioned in the presentation
The above PPT contains the following content:
1. SPREADING OF VIRUS
2. ANAMNESIS (CASE STUDIES)
3. CURRENT STATUS OF MOBILE MALWARE
4. PROTECTIVE MEASURES
5. THREATS OF MOBILE PHONE
6. CONCLUSION
The detailed PROTECTIVE MEASURES are given in the above PPT.
This document provides conversion factors and abbreviations for various units of measurement in the metric system including length, area, volume, weight, temperature, time, speed, force, pressure, power, angle, and other physical quantities. Some key conversions include:
- 1 meter = 100 centimeters
- 1 square meter = 10,000 square centimeters
- 1 liter = 1,000 cubic centimeters
- 1 kilogram = 1,000 grams
- 0 degrees Celsius = 32 degrees Fahrenheit
- 1 newton = 100,000 dynes
- 1 bar = 100,000 pascals
Cybercrime involves using computers or the internet to facilitate illegal activities such as hacking, identity theft, or distributing malware. The document discusses the history and types of cybercrimes such as unauthorized access, hacking, software piracy, and cyberterrorism. Laws against cybercrimes in India are outlined, as well as tips to prevent cybercrimes like using strong passwords, antivirus software, and keeping systems updated. While cybercrime poses challenges, individuals and governments can work together to protect against harmful hackers and cybercriminals.
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Creativity for Innovation and SpeechmakingMattVassar1
Tapping into the creative side of your brain to come up with truly innovative approaches. These strategies are based on original research from Stanford University lecturer Matt Vassar, where he discusses how you can use them to come up with truly innovative solutions, regardless of whether you're using to come up with a creative and memorable angle for a business pitch--or if you're coming up with business or technical innovations.
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 3)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
Lesson Outcomes:
- students will be able to identify and name various types of ornamental plants commonly used in landscaping and decoration, classifying them based on their characteristics such as foliage, flowering, and growth habits. They will understand the ecological, aesthetic, and economic benefits of ornamental plants, including their roles in improving air quality, providing habitats for wildlife, and enhancing the visual appeal of environments. Additionally, students will demonstrate knowledge of the basic requirements for growing ornamental plants, ensuring they can effectively cultivate and maintain these plants in various settings.
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.
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 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.
2. INTRODUCTION
Facial recognition (or face recognition) is a type of biometric software
application that can identify a specific individual in a digital image by analyzing and
comparing patterns.
Facial recognition systems are commonly used for security purposes but are
increasingly being used in a variety of other applications. For example, Facebook uses
facial recognition software to help automate user tagging in photographs.
1. What are biometrics?
Ans: A biometric is a unique, measurable characteristic of a human being that can be
used to automatically recognize an individual or verify an individual identity. Biometrics
can measure both physiological and behavioral characteristics.
Physiological biometrics (based on measurements and data derived from direct the
human body) include:
a. Finger-scan ,
b. Facial Recognition,
c. Iris-scan ,
d. Retina-scan and
e. Hand-scan.
Behavioral biometrics (based on measurements and data derived from an action)
include:
a. Voice-scan ,
b. Signature-scan and
c. Keystroke-scan .
3. FACE RECOGNITION
The face is an important part of who you are and how people identify you.
For face recognition there are two types of comparisons.
The first is verification and the second is identification.
verification is where the system compares the given individual with who that
individual says they are and gives a yes or no decision..
identification is where the system compares the given individual to all the
Other individuals in the database and gives a ranked list of matches.
All identification or authentication technologies operate using the following
four stages:
1. Capture: A physical sample is captured by the system during enrollment and
also in identification or Verification process.
2. Extraction: unique data is extracted from the sample and a template is
created.
3. Comparison: the template is then compared with a new sample.
4. Match/Non match: the system decides if the features extracted from the
new
4. CAPTURING OF IMAGE BY STANDARD
VIDEO CAMERAS
The image is optical in characteristics and may be thought of as a collection of a
large number of bright and dark areas representing the picture details.
In other words the picture information is a function of two variables:
Time and Space.
It would require infinite number of channels to transmit optical information
corresponding to picture elements simultaneously. There is practical difficulty in
transmitting all information simultaneously so we use a method called scanning.
5. COMPONENTS OF FACE RECOGNITION
SYSTEMS
The 3 main components of face recognition systems, they are as follows
Enrollment module,
Database and
Identification module.
6. HOW FACE RECOGNITION SYSTEMS WORK
Facial recognition software is based on the ability to first recognize faces, which
is a technological feat in itself.
If you look at the mirror, you can see that your face has certain distinguishable
landmarks. These are the peaks and valleys that make up the different facial features.
There are about 80 nodal points on a human face. Here are few nodal points that
are measured by the software.
• Distance between the eyes
• Width of the nose
• Depth of the eye socket
• Cheekbones
• Jaw line and
• Chin
These nodal points are measured to create a numerical code, a string of numbers
that represents a face in the database. This code is called face print.
Only 14 to 22 nodal points are needed for faceit software to complete the
recognition process
7. IMPLEMENTATION OF FACE RECOGNITION
TECHNOLOGY
The implementation of face recognition technology includes the following four
stages:
1. Data acquisition,
2. Input processing ,
3. Face image classification and
4. Decision making .
8. PERFORMANCE
1. False rejection rates (FRR) :
The probability that a system will fail to identify an enrollee. It is also called
type 1 error rate.
FRR= NFR/NEIA
Where
FRR= false rejection rates
NFR= number of false rejection rates
NEIA= number of enrollee identification attempt
2. False acceptance rate (FAR) :
The probability that a system will incorrectly identify an individual or will fail
to reject an imposter. It is also called as type 2 error rate
FAR= NFA/NIIA
Where
FAR= false acceptance rate
NFA= number of false acceptance
NIIA= number of imposter identification attempts
9. SOFTWARES
Facial recognition software falls into a larger group of technologies known as
biometrics. Facial recognition methods may vary, but they generally involve a series of
steps that serve to capture, analyze and compare your face to a database of stored images.
The basic process that is used by the Faceit system to capture and compare
images:
1. Detection,
2. Alignment,
3. Normalization,
4. Representation and
5. Matching.
10. Advantages :
1. There are many benefits to face recognition systems such as its convenience
and Social acceptability. All you need is your picture taken for it to work.
2. Face recognition is easy to use and in many cases it can be performed without
a Person even knowing.
3. Face recognition is also one of the most inexpensive biometric in the market
and Its price should continue to go down.
ADVANTAGES AND DISADVANTAGES
Disadvantage:
1. Face recognition systems cant tell the difference between identical twins.
11. There are numerous applications for face recognition technology:
Commercial Use:
a. Day Care: Verify identity of individuals picking up the children.
b. Residential Security: Alert homeowners of approaching personnel
c. Voter verification: Where eligible politicians are required to verify
their identity during a voting process.
d. Banking using ATM: The software is able to quickly verify a customer.
APPLICATIONS
12. Face recognition technologies have been associated generally with very costly
top secure applications. Today the core technologies have evolved and the cost of
equipment is going down dramatically due to the integration and the increasing
processing power. Certain applications of face recognition technology are now cost
effective, reliable and highly accurate.
CONCLUSION
13. THANK YOU
For any mistakes and suggestion’s feel free to text here
idamakanti.jaswanth@gmail.com