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Face Recognition based Attendance Management System

Face Recognition based Attendance Management
Smitha, Pavithra S Hegde, Afshin
Dept. of Computer Science and Engineering
Yenepoya Institute of Technology
Moodbidri, India
Abstract In this digital era, face recognition system plays a
vital role in almost every sector. Face recognition is one of the
mostly used biometrics. It can used for security, authentication,
identification, and has got many more advantages. Despite of
having low accuracy when compared to iris recognition and
fingerprint recognition, it is being widely used due to its
contactless and non-invasive process. Furthermore, face
recognition system can also be used for attendance marking in
schools, colleges, offices, etc. This system aims to build a class
attendance system which uses the concept of face recognition as
existing manual attendance system is time consuming and
cumbersome to maintain. And there may be chances of proxy
attendance. Thus, the need for this system increases. This system
consists of four phases- database creation, face detection, face
recognition, attendance updation. Database is created by the
images of the students in class. Face detection and recognition is
performed using Haar-Cascade classifier and Local Binary
Pattern Histogram algorithm respectively. Faces are detected
and recognized from live streaming video of the classroom.
Attendance will be mailed to the respective faculty at the end of
the session.
KeywordsFace Recognition; Face Detection; Haar-Cascade
classifier; Local Binary Pattern Histogram; attendance system;
Traditional method of attendance marking is a tedious task
in many schools and colleges. It is also an extra burden to the
faculties who should mark attendance by manually calling the
names of students which might take about 5 minutes of entire
session. This is time consuming. There are some chances of
proxy attendance. Therefore, many institutes started
deploying many other techniques for recording attendance
like use of Radio Frequency Identification (RFID) [3], iris
recognition [4], fingerprint recognition, and so on. However,
these systems are queue based which might consume more
time and are intrusive in nature.
Face recognition has set an important biometric feature,
which can be easily acquirable and is non-intrusive. Face
recognition based systems are relatively oblivious to various
facial expression. Face recognition system consists of two
categories: verification and face identification. Face
verification is an 1:1 matching process, it compares face
image against the template face images and whereas is an 1:N
problems that compares a query face images [1].
The purpose of this system is to build a attendance system
which is based on face recognition techniques. Here face of
an individual will be considered for marking attendance.
Nowadays, face recognition is gaining more popularity and
has been widely used. In this paper, we proposed a system
which detects the faces of students from live streaming video
of classroom and attendance will be marked if the detected
face is found in the database. This new system will consume
less time than compared to traditional methods.
Authors in [3] proposed a model of an automated
attendance system. The model focuses on how face
recognition incorporated with Radio Frequency Identification
(RFID) detect the authorized students and counts as they get
in and get out form the classroom. The system keeps the
authentic record of every registered student. The system also
keeps the data of every student registered for a particular
course in the attendance log and provides necessary
information according to the need.
In this paper [4], authors have designed and implemented
an attendance system which uses iris biometrics. Initially, the
attendees were asked to register their details along with their
unique iris template. At the time of attendance, the system
automatically took class attendance by capturing the eye
image of each attendee, recognizing their iris, and searching
for a match in the created database. The prototype was web
In [5], authors proposed an attendance system based on
facial recognition. The algorithms like Viola-Jones and
Histogram of Oriented Gradients (HOG) features along with
Support Vector Machine (SVM) classifier were used to
implement the system. Various real time scenarios such as
scaling, illumination, occlusions and pose was considered by
the authors. Quantitative analysis was done on the basis of
Peak Signal to Noise Ratio (PSNR) values and was
implemented in MATLAB GUI.
Authors in [6] researches to get best facial recognition
algorithm (Eigenface and Fisherface) provided by the Open
CV 2.4.8 by comparing the Receiver Operating
Characteristics (ROC) curve and then implemented it in the
attendance system. Based on the experiments carried out in
this paper, the ROC curve proved that, Eigenface achieves
better result than Fisherface. System implemented using
Eigenface algorithm achieved an accuracy rate of 70% to
In [7], authors proposed a method for student attendance
system in classroom using face recognition technique by
combining Discrete Wavelet Transforms (DWT) and Discrete
Cosine Transform (DCT). These algorithms were used to
extract the features of student’s face followed by applying
Radial Basis Function (RBF) for classifying the facial
objects. This system achieved an accuracy rate of 82%.
All the students of the class must register themselves by
entering the required details and then their images will be
International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181
IJERTV9IS050861 (This work is licensed under a Creative Commons Attribution 4.0 International License.)
Published by :
Vol. 9 Issue 05, May-2020
captured and stored in the dataset. During each session, faces
will be detected from live streaming video of classroom. The
faces detected will be compared with images present in the
dataset. If match found, attendance will be marked for the
respective student. At the end of each session, list of
absentees will be mailed to the respective faculty handling
the session.
The system architecture of the proposed system is given
Fig.1. System Architecture
Typically this process can be divided into four stages,
1. Dataset Creation
Images of students are captured using a web cam. Multiple
images of single student will be acquired with varied gestures
and angles. These images undergo pre-processing. The
images are cropped to obtain the Region of Interest (ROI)
which will be further used in recognition process. Next step is
to resize the cropped images to particular pixel position. Then
these images will be converted from RGB to gray scale
images. And then these images will be saved as the names of
respective student in a folder.
2. Face Detection
Face detection here is performed using Haar-Cascade
Classifier with OpenCV. Haar Cascade algorithm needs to be
trained to detect human faces before it can be used for face
detection. This is called feature extraction. The haar cascade
training data used is an xml file-
haarcascade_frontalface_default. The haar features shown in
Fig.2. will be used for feature extraction.
Fig.2. Haar Features
Here we are using detectMultiScale module from OpenCV.
This is required to create a rectangle around the faces in an
image. It has got three parameters to consider- scaleFactor,
minNeighbors, minSize. scaleFactor is used to indicate how
much an image must be reduced in each image scale.
minNeighbors specifies how many neighbors each candidate
rectangle must have. Higher values usually detects less faces
but detects high quality in image. minSize specifies the
minimum object size. By default it is (30,30) [8]. The
parameters used in this system is scaleFactor and
minNeighbors with the values 1.3 and 5 respectively.
3. Face Recognition
Face recognition process can be divided into three steps-
prepare training data, train face recognizer, prediction. Here
training data will be the images present in the dataset. They
will be assigned with a integer label of the student it belongs
to. These images are then used for face recognition. Face
recognizer used in this system is Local Binary Pattern
Histogram. Initially, the list of local binary patterns (LBP) of
entire face is obtained. These LBPs are converted into
decimal number and then histograms of all those decimal
values are made. At the end, one histogram will be formed
for each images in the training data. Later, during recognition
process histogram of the face to be recognized is calculated
and then compared with the already computed histograms and
returns the best matched label associated with the student it
belongs to [9].
4. Attendance Updation
After face recognition process, the recognized faces will be
marked as present in the excel sheet and the rest will be
marked as absent and the list of absentees will be mailed to
the respective faculties. Faculties will be updated with
monthly attendance sheet at the end of every month.
The users can interact with the system using a GUI. Here
users will be mainly provided with three different options
such as, student registration, faculty registration, and mark
attendance. The students are supposed to enter all the
required details in the student registration form. After
clicking on register button, the web cam starts automatically
and window as shown in Fig.3. pops up and starts detecting
the faces in the frame. Then it automatically starts clicking
photos until 60 samples are collected or CRTL+Q is pressed.
These images then will be pre-processed and stored in
training images folder.
The faculties are supposed to register with the respective
course codes along with their email-id in the faculty
registration form provided. This is important because the list
of absentees will be ultimately mailed to the respective
International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181
IJERTV9IS050861 (This work is licensed under a Creative Commons Attribution 4.0 International License.)
Published by :
Vol. 9 Issue 05, May-2020
Fig.3. Face Detection
In every session, respective faculty must enter their course
code. Then after submitting the course code, the camera will
start automatically. The Fig.4. shows the face recognition
window where two registered students are recognized and if
in case they were not registered it would have shown
‘unknown’. By pressing CTRL+Q, the window will be closed
and attendance will be updated in the excel sheet and names
of absentees will be mailed to the respective faculty.
Fig.4. Face Recognition
Fig.5. Attendance sheet
The Fig.5. shows the attendance sheet updated after
recognition process. Recognized students are marked as ‘1’
and absent students are marked as ‘0’. The list of absentees
will be mailed to the respective faculty email-id.
This system aims to build an effective class attendance
system using face recognition techniques. The proposed
system will be able to mark the attendance via face Id. It will
detect faces via webcam and then recognize the faces. After
recognition, it will mark the attendance of the recognized
student and update the attendance record.
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International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181
IJERTV9IS050861 (This work is licensed under a Creative Commons Attribution 4.0 International License.)
Published by :
Vol. 9 Issue 05, May-2020
... Utilizing an automated attendance system is a recent method of keeping track of attendance. Several attendance tracking systems that make use of biometric recognition have been presented, including Fingerprint Recognition (Mohamed and Raghu [3]; Soewito et al. [4]), Face Recognition (Varadharajan et al. [5], Smitha [6]; Ofualagba et al. [7]; Alburaiki et al. [8]), Barcodes (Noor et al. [9]), QR Codes (Sunaryono et al. [10], Sutar et al. [11]), GPS (Kumar and Kumar [12]), Jetson Nano (Chandramouli et al. [13]), Sensors (Prangchumpol [14]), RFID (Souza et al. [15]), Authorized Username and Password Mobile Device (Kumbhar et al. [16]; Hameed [17]) to obtain student ID for the attendance process. Many systems have been installed on Portable Devices (Chandramouli et al. [13], Varadharajan et al. [5], Mendonca et al. [18]) and Smartphones (Souza et al. [15], Prangchumpol [14], Kumar and Kumar [12], Somasundaram et al. [19]; Noor et al. [9]; Soewito et al. [4]; Kumbhar et al. [16]; Hameed [17]). ...
... A face recognition system is a type of tech that can compare face images from a video or photograph to a database of known and unknown faces. The Face, Recognition-based Attendance Management System, was developed by Smitha to develop an organized classroom attendance system using face recognition methods [6]. Through facial ID, the system can record involvement. ...
... At each class, faces will be detected from a live video of the classroom and will mark attendance [6]. ...
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... Also, the days when people generally tend to take leaves. Office hour's data can help improve employability efficiency as an analysis of break hours on a general level could be helpful to calculate average continuous work hours of the employees before they need a break [3,4]. ...
... Traditional method of marking attendance is time taking process and there may a chance of proxy attendance, so institute started using RFID, Face Recognition type attendance systems. Face recognition used Haar Cascade Classifier with MATLAB GUI [3]. ...
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Automated Attendance System Using Image Processing
  • Hapani
  • Smit
Hapani, Smit, et al. "Automated Attendance System Using Image Processing." 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018.
Face Recognition and RFID Verified Attendance System
  • Md Akbar
  • Sajid
Akbar, Md Sajid, et al. "Face Recognition and RFID Verified Attendance System." 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE). IEEE, 2018.
Implementation of face recognition algorithm for biometrics based time attendance system
  • Adrian Rhesa Siswanto
  • Anto Septian
  • Maulahikmah Satriyo Nugroho
  • Galinium
Siswanto, Adrian Rhesa Septian, Anto Satriyo Nugroho, and Maulahikmah Galinium. "Implementation of face recognition algorithm for biometrics based time attendance system." 2014 International Conference on ICT For Smart Society (ICISS). IEEE, 2014.