IRJET- Automated Attendance System using Face Recognition

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7118
AUTOMATED ATTENDANCE SYSTEM USING FACE RECOGNITION
Abhilasha Varshney1, Sakshi Singh2, Suneet Srivastava3, Suyash Chaudhary4, Tanuja5
1Assistant Professor, Dept. of Information Technology, Inderprastha Engineering College Ghaziabad, U.P, India.
2,3,4,5Student, Dept. of Information Technology, Inderprastha Engineering College Ghaziabad, U.P, India.
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Abstract - Face recognition is a big research area which
takes more attention of many researchers in computer
technology. The human face recognition from video sequences
is a challenging task, because there are variations present in
the background of the images, facial expression and
illumination. Our aim to develop an automated attendance
management system which can capture and mark presence of
students as well as employees using video frames with deep
learning method. This system is able to capture real time video
and can generate attendance report with improved accuracy.
Keywords- Video Framing, Video Recognition, Deep
Learning, LBPH, Automated Attendance.
1. INTRODUCTION
Face recognition is a major challenge encountered in
multidimensional visual model analysis and is a hot area of
research. The art of recognizing the human face is quite
difficult as it exhibits varyingcharacteristicslikeexpressions,
age, change in hairstyle etc. Although many methods have
been proposed to detect and recognize human face
developing a computational model for alargedatabaseisstill
challenging task.Thatiswhyfacerecognitionisconsideredas
high level computer vision task in which techniques can be
developed to achieve accurate results. Few popular methods
known for face recognition are neural network group based
tree, neural nets, artificial neural networks and principal
component analysis. The recognition of the face from videos
has numerous applications in Video Computer Vision. The
main challenge of detecting face image in videos is the pose
and the illumination variations and sudden changes in the
movement of the object. Themainchallengesofdesigningthe
robust face recognition algorithms are pose variation, self-
occlusion of facial feature. The use of Multi view data to
handle the pose variation and its challenges. Multi-camera
network commonly used for biometric and surveillance
system, multiple view point overcomethedrawbackofsingle
view point. For example multiple view point increases the
position of the person in differentpose.Theproposedsystem
analyzes and recognizes the exact face image from the video
even though there are pose variation and illumination
variation while the existing systems deals with the
recognition of the face images .
2. LITERATURE REVIEW
The objective of this system is to present an automated
system forhuman face recognition in a real timebackground
for an organization to mark the attendance of their
employees or student. So automated attendance using real
time face recognition is a real world solution which comes
with day to day activities of handling employees or student.
The task is very difficult as the real time background
subtraction in an image is still a challenge. In the past two
decades, face detection and recognitionhasproventobevery
interesting research field of image processing. The work
carried out describes an automated attendance systemusing
video surveillance. The proposed algorithm is automatic and
efficient in intelligent surveillance applications. Video
surveillance is used to detect the object movement thereby
the captured image undergoesfacedetectionandrecognition
process and searches the student database and enters the
attendance if it is valid in the list.
This paper uses Local Binary Pattern Histogram (LBPH)
algorithm for face detection and correlations formulas for
face recognition. It was first described in 1994 and has since
been found to be a powerful featurefortextureclassification.
It has further been determined that when LBP is combined
with histograms of oriented gradients (HOG) descriptor, it
improves the detection performance considerably on some
datasets. The primary purpose of this paper review is to find
the solutions provided by others author and consider the
imperfection of the system proposed by them, give the best
solutions.
In [4] Kawaguchi introduced a lecture attendance system
with a new method called continuous monitoring, and the
automatically by the camera which captures the student’s
attendance marked photo of a student in the class. The
architecture of the system is simple since two cameras
equipped with the wall of the class. The first one is a
capturing camera used to capture the image student in the
class and the second camera is sensor camera is used to
getting the seat of a student inside the class and the camera
capturing will snap the image of the student. The system
compares the picturetaking from a camera capturingimages
and faces in the database done much time to perfect the
attendance.
Other paperproposedby[5]introducedareal-timecomputer
vision algorithm in automatic attendance management
system. The system installed the camera with non-intrusive,
which can snap images in the classroom and compared the
extracted face from the image of the camera capturing with
faces inside the system. This system also used machine
learningalgorithmwhichareusuallyusedincomputervision.
Also, HAAR CLASSIFIERS used to train the images from the
camera capturing. The face snap bythecameracapturingwill
convert to grayscale and do subtraction on the images; then
the image is transferred to store ontheserverandprocessing
later.

In 2012 N. Kar [6] introduced an automated attendance
management system using face recognition technique which
used the Principal Component Analysis To implementation
the system, use two libraries such OpenCV is a computer
vision library and FLTK(Light Tool Kit. Both of this libraries
helped the development such as OpenCV support algorithm
and FLTK used to design the interface. In the system, there
are Request Matching and Adding New fact to Database. In
Request Matching, the first step is open the camera and snap
the photo after the extractionthe frontalface.Thenextstepis
recognizing the face with the training data and project the
extracted face onto the Principal Component Analysis. The
final step displays the nearest face with the acquired images.
Apart from that, adding a new face into the database is snap
the photo after that extract the frontal face images and then
perform the Haar cascade Method to find the object in the
image in different window size. The next step is to store the
image into the database and to learn the face then perform
the Principal Component AnalysisAlgorithm.Thefinalstepis
storing the information inside thefaceXMLfile.Thesystemis
focused on the algorithm to improve the face detection from
acquired images or videos.
In [9] the author also proposed a system which implements
automatic attendance using face recognition. The system
which can extract the object in the face such nose, mouth by
usingMATLABwithPrincipalComponentAnalysis(PCA).The
system [7] designed to resolve the issues of attendance
marking system such as time-consuming. As the result of the
experiment show that this paper, the systemcanrecognizein
case the dark background or difference view ofthefaceinthe
classroom. Jyotshana Kanti [8] proposed a smart attendance
marking system whichcombinestwodifferencingalgorithms
such Principal Component Analysis and Artificial Neural
Network. The purpose of the author is tosolvethetraditional
attendance marking resolve the time-consuming. In the
systemimplementwithPrincipalComponentAnalysis,itdoes
an extraction and identify thesimilaritiesofthefacedatabase
and acquire images. Artificial NeuralNetworkisusedtosolve
the problem of the input data or learn from the input data,
and the expect value. In the system implemented by the
author using back propagation algorithmand combineswith
mathematical function to perform in that system. As a result,
written by the author research, it shows that the system can
use to recognize in a different environment.
In [7] Priyanka Thakare proposed a method using Eigenface
and PrincipalComponentAnalysiswhichhasthearchitecture
as the following step. The camera needs to install in the front
which can capture an entire face of the student inside the
class. The first phase after thecamera has been captured; the
captured image was transferred into the system as an input.
The image capturefromthecamerasometimescomewiththe
darkness or brightness which need to do an enhancementon
it such as convert to gray image. The next step, Histogram
Normalization is used in this system remove the contrast of
the image. It is easy to recognize when has the student sit in
the back row. The Median filter is used to remove noise from
the image in case the camera is high definition camera, but
sometimes it still contains the noise. The author also
implements with skin classification which changes all the
pixel to black except the pixel are close to the skin.
3. PROPOSED WORK
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 assistthefaculty
for maintaining attendance record automatically.
In this paper we propose an automated attendance
management system. This system, which is based on face
detection and recognition algorithms, automatically detects
the student when he enters the class room and marks the
attendance by recognizing him. Taking attendance using
traditional attendance marking system has been a tedious
task as it involves a lot of paperwork, and there are chances
of redundant attendance, and is highly inefficient. Machine
Learning on FacialRecognition The approachwearegoingto
use for facial recognition is very straight forward .Let’s see
how modern face recognition works.
The goal here is to get deep neural network to output a
person’s face with identification. This means that the neural
network needs to be trained to automatically identify
different features of a face and calculate numbers based on
that. The output of the neural networkcanbethoughtofasan
identifier for a particular person’s face—if you pass in
different images of the same person, the output of the neural
network will be very similar or close, whereas if you pass in
images of a different person, the output willbeverydifferent.
Model and their training
The model is designed to be a GUI based project. To serve the
image to the model, the id and the name is entered and then
the sample images are taken by “Take image” button. Once
you click the take image button the camera of the system
opens up and the 60 image samples of a particular person is
taken for accuracy of the system. The camera then closes
once it is done with 60 image samples and once the samples
are been taken then at the Notification text fieldyou'llseethe
status of the model.
Now the next step will be to train the model of the image
sample taken. In this step the “TrainImages”buttonisclicked
and internally the algorithm train the model by the image
samples taken from the previous step. And then again at the
notification text field the status of the model is again shown.
Approach towards solution
The system consists of a camera that records the video of the
classcaptures the images of the person thentheimageissent
to the image enhancement module. After enhancement the

image comes in the Face Detection and Recognition modules
and then the attendance is marked. At the time of enrolment,
samples of face images of individual personsarestoredinthe
Face database. Here all the faces are detected from the input
image and the algorithm compares them one by one with the
face dataset. If any face is recognized the attendance is
marked. Human beings perform face recognition
automatically every day and practically with no effort.
Although it sounds like a very simple task for us, it has
proven to be a complex task for a computer, as it has many
variables that can impair the accuracy of the methods, for
example: illumination variation, low resolution, occlusion,
amongst other. In computer science, face recognition is
basically the task of recognizing a person based on its facial
image. It has become very popular in the last two decades,
mainly because of the new methods developed and the high
quality of the current videos/cameras.
Face Detection: It has the objective of finding the faces
(location and size) in an image and probably extract them to
be used by the face recognition algorithm.
Face Recognition: with the facial images already extracted,
cropped, resized and usually converted to grayscale, the face
recognition algorithm is responsible for finding
characteristics which best describe the image.
The face recognition systems can operate basically in two
modes:
Verificationorauthenticationofafacialimage:Itbasically
compares the input facial image with the facial image related
to the user which is requiring the authentication. It is
basically a 1x1 comparison.
Identification or facial recognition: It basically compares
the input facial image with all facial images from a dataset
with the aim to find the user that matches that face. It is
basically a 1xN comparison.
Algorithm Used : Local Binary Pattern (LBP) is a simple yet
very efficient texture operator which labels the pixels of an
image by thresholding the neighborhood of each pixel and
considers the resultasa binarynumber.Itwasfirstdescribed
in 1994 (LBP) and has since been found to be a powerful
feature for texture classification. It has further been
determined that when LBP is combined with histograms of
oriented gradients (HOG) descriptor, it improves the
detection performance considerably onsomedatasets.Using
the LBP combined with histograms wecan representtheface
images with a simple data vector. As LBP 1s a visual
descriptor it can also be used for face recognition tasks, as
can be seen in the following step-by-step explanation.
Steps:
1. Parameters: the LBPH uses 4 parameters:
Radius: the radius is used to build the circular local binary
pattern and represents the radiusaround the central pixel. It
is usually set to 1.
Neighbors: the number of sample pointstobuildthecircular
local binary pattern. Keep in mind: the more sample points
you include, the higher the computational cost. It is usually
set to 8.
Grid X: the number of cells in the horizontal direction. The
more cells, the finer the grid, the higher the dimensionalityof
the resulting feature vector. It is usually set to 8.
Grid Y: the number of cells in the verticaldirection.Themore
cells, the finer the grid, the higher the dimensionality of the
resulting feature vector. It is usually set to 8.
Don’t worry about the parameters right now, you will
understand them after reading the next steps.
2.Training the Algorithm: First, we need to train the
algorithm. To do so, we need to use a dataset with the facial
images of the people we want to recognize. We need to also
set an ID (it may be a number or the name of the person) for
each image, so the algorithm will use this information to
recognize an input image and give you an output. Images of
the same person must have the same ID. With the training
set already constructed, let’s see the LBPH computational
steps.
3.Applying the LBP operation: The first computational step
of the LBPH is to create an intermediateimagethatdescribes
the original image in a better way, by highlighting the facial
characteristics. To do so, the algorithm uses a concept of a
sliding window, based on the parameters radius and
neighbors.
The image below shows this procedure:
Based on the image above, let’s break it into several small
steps so wecan understand iteasily:Supposewehaveafacial
image in grayscale. We can get part of this imageasawindow
of 3x3 pixels.
It can also be represented as a 3x3 matrix containing the
intensity of each pixel (0~255). Then, we need to take the
central value of the matrix to be used as the threshold. This
value will be used to define the new values from the 8
neighbors.
For each neighbor of the central value (threshold), we set a
new binary value. We set 1 forvaluesequalorhigherthanthe
threshold and 0 for values lower than the threshold.
Now, the matrix will contain only binary values (ignoringthe
central value). We need to concatenate each binary value
from each position from the matrix line by line into a new
binary value (e.g. 10001101). Note: some authors use other

approaches to concatenate the binary values (e.g. clockwise
direction), but the final result will be the same.
Then, we convert this binary value to a decimal value and set
it to the central value of the matrix, which is actually a pixel
from the original image. At the end of this procedure (LBP
procedure), we have a new image which represents better
the characteristics of the original image.
Note: The LBP procedure was expanded to use a different
number of radius and neighbors, it is called Circular LBP.
It can be done by using bilinear interpolation. If some data
point is between the pixels, it uses the values from the 4
nearest pixels (2x2) to estimate the value of the new data
point.
4.Extracting theHistograms: Now,usingtheimagegenerated
in the last step, we can use the Grid X and Grid Y parameters
to divide the image into multiple grids, as can be seen in the
following image:
Based on the image above, we can extract the histogram of
each region as follows:
As wehave an image in grayscale, each histogram (fromeach
grid) will contain only 256 positions (0~255) representing
the occurrences of each pixel intensity. Then, we need to
concatenate each histogram to create a new and bigger
histogram. Supposing we have 8x8 grids, we will have
8x8x256=16.384 positions in the final histogram. The final
histogram representsthecharacteristicsoftheimageoriginal
image.
The LBPH algorithm is pretty much it.
5.Performing the face recognition: In this step,thealgorithm
is already trained. Each histogram created is used to
represent each image from the training dataset. So, given an
input image, we perform the steps again for this new image
and creates a histogram which represents the image. So to
find the image that matches the input image we just need to
compare two histograms and return the image with the
closest histogram.
We can use various approaches to compare the histograms
(calculate the distance between two histograms), for
example: Euclidean distance, chi-square, absolute value, etc.
So the algorithm output is the ID from the image with the
closest histogram. The algorithm should also return the
calculated distance, which can be used as a ‘confidence’
measurement. Note: don’t be fooled about the ‘confidence’
name, as lower confidences are better because it means the
distance between the two histograms is closer.
We can then use a threshold and the ‘confidence’ to
automatically estimate if the algorithm has correctly
recognized the image. We can assume that the algorithm has
successfully recognized if the confidence is lower than the
threshold defined.
4. RESULT
Attendance System is the advancement that has taken place
in the field of automation replacing traditional attendance
marking activity. We have projected our ideas to implement
“Automated Attendance System Based on Face Recognition’,
in which it imbibes large applications. The application
includes face identification, which saves timeand 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.
Figure 4.1: Face Detection
Figure 4.2: Data Saved

Figure 4.3: Model Trained
Figure 4.4: Records
Figure 4.5: Face Recognition
5. CONCLUSIONS
This system has been proposed for maintaining the
attendance record. The main motive behind developing this
system is to eliminate all the drawbacks which were
associated with manual attendance system.
The drawbacks ranging from wastage of time and paper, till
the proxy issues arising in a class, will completely be
eliminated.
Hence, desired resultswithuserfriendlyinterfaceisexpected
in the future, from the system. The efficiency of the system
could also be increased by integrating various steps and
techniques in the futuredevelopingstagesofthesystemused
for face detection. Where it is used in both creating database
and face recognitionprocess.Whereincasecreatingdatabase
it takes input image through a web camera continuously.
Captured image undergoes face detection. Detected face will
be cropped and stored in database. Where in case of face
recognition if there is any movement video surveillance will
be used to detect the moving object. The captured image
undergoes face detection andfurther processed laterby face
Cross-Correlation and Normalized-Correlation are used to
extract the Coordinates of peak with the target images. The
peak of the cross-correlation matrix occurs where the sub
images are best correlated. Find the total offset between the
images. The total offset or translation between images
depends on the location of the peak in the cross correlation
matrix, and on the size and position of the sub images. Check
if the face is extracted from the target Image. Figure out
where face exactly matches inside of target image.
ACKNOWLEDGEMENT
It gives us great pleasure in presenting this project synopsis
report titled: “ Automated Attendance System using Face
detection”. We expressourgratitudetoourprojectguideMrs.
Abhilasha Varshney (Assistant Professor), who provided us
with all the guidance and encouragement. Wealsowouldlike
to deeply express our sincere gratitude to the Head of the
Information Technology Department Dr. Pooja Tripathi, for
her approval to this project. We are also thankful to her for
providing us the needed assistance,detailedsuggestionsand
also encouragement to this project. We would like to deeply
express our sincere gratitude to our respected director
Dr.B.C.Sharma and the management of Inderprastha
Engineering College for providing such an ideal atmosphere
to build up this project with well-equipped and up to date IT
Laboratories. We are extremely thankful to all the staff and
the management of the college for providing us all the
facilities and resources required.
REFERENCES
[1] K.Senthamil Selvi, P.Chitrakala, A.Antony
Jenitha,”Face Recognition Based Attendance MarkingSystem”,
IJCSMC, Vol. 3, Issue. 2, February 2014
[2] Narayan T. Deshpande, Dr. S.Ravishankar, ”Face
Detection and Recognition using Viola-Jones algorithm and
Fusion of PCA and ANN”, Advances in Computational
Sciences and Technology ISSN 0973-6107 Volume 10,
Number 5 (2017)
[3] Pooja Malusare , Shivangi Shewale,”Face and Person
Recognition from Unconstrained Video”, IJESC Vol. 7 Issue
No. 2, February 2017.
[4] Y. Kawaguchi, “Face Recognition-basedLectureAttendance
System,” 3rd AEARU no. October, 2005.
[5] V. Shehu and A. Dika, “Using real time computer vision
algorithms in automaticattendancemanagementsystems,”Inf.
Technol. Interfaces (ITI), 2010 32nd Int. Conf., pp. 397–402,
2010.

[6] N. Kar, M. K. Debbarma, A. Saha, and D. R. Pal, “Study of
Implementing Automated Attendance System Using Face
Recognition Technique,” Int. J. Comput. Commun. Eng., vol. 1,
no. 2, pp. 100–103, 2012.
[7] P. Wagh, R. Thakare, J. Chaudhari, and S. Patil, “Attendance
system based on face recognition using eigenfaces and PCA
algorithms,” in 2015 International Conference on Green
Computing and Internet of Things (ICGCIoT), 2015, pp. 303–
308
[8] J. Kanti and A. Papola, “Smart Attendance using Face
Recognition with Percentage Analyzer,” vol. 3, no. 6, pp. 7321–
7324, 2014.
[9] J. Joseph and K. P. Zacharia, “Automatic Attendance
Management System Using Face Recognition,” Int. J. Sci. Res.,
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