ATTENDANCE MANAGEMENT VIA FACIAL RECOGNITION

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ATTENDANCE MANAGEMENT VIA FACIAL RECOGNITION
Aditya Sengar1, Mrityunjay Mishra2, Naveen Kumar Yadav3, Vikas Malviya4
Under the guidance of
Gagandeep Singh : Assistant Professor
LOVELY PROFESSIONAL UNIVERSITY
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Abstract - Bio-metric authentication is being widely
used and it is replacing traditional systems with its main
features being fast and reliable. There are different methods
for biometric security such as systems based on fingerprint
scanners, facial recognition, iris scanner, and voice
recognition. Fingerprint scanner is extensively used in most
the places for purpose of authentication but their major
drawback can be hygiene. Facial recognition is emerging as
a popular technology in this era. There has been a
significant advancement in the field of facial recognition for
the purpose of biometric security. It is really challenging to
remember different passwords for various accounts, but in
the system which uses facial recognition we don’t have to
remember the passwords, we become the password. Facial
recognition is majorly used in fields of defense, security,
smartphones, and banking, but this system can be further
developed to be incorporated into our day-to-day life for
attendance marking in schools and offices and also by law
enforcement to identify criminals or lawbreakers. Our
project is the integration of web development, machine
learning, and database with a user-friendly interface with
the help of this system many organizations such as schools,
and corporate office can change their conventional method
of attendance management.
Key Words : PYTHON; FACE RECOGNITION; IMAGE
PROCESSING; DJANGO; OPENCV; HOG+SVM
1. INTRODUCTION
This project attendance management system uses facial
recognition for marking attendance, time of entrance, time
of departure, and maintaining attendance records of
students. This is an efficient replacement for the traditional
attendance system that is done manually. it covers areas of
computer vision (face detection, alignment, and
recognition), machine learning, and web application
development to implement various functionalities namely
student registration, creation of dataset, training of ml
model on the dataset, and view attendance reports, etc. This
project can be extremely useful for maintaining attendance
records in schools and various organizations.
2. EXISTING SYSTEM
Over the last few years, a variety of attendance
management systems based on facial recognition have
been deployed in various organizations to enhance the
performance of employees and students. J. Joseph and K
Zacharia suggested a Matlab-based system that included
PCA, Eigenfaces, image processing, and a micro-controller.
The system works with facial picture data, and a suitable
method that works with the systems’ orientation is
required. A. Patil and colleagues introduced a face
identification approach for attendance marking based on
Haar cascades and the Viola-Jones algorithm. The system of
artificial neural networks was proposed , which is both
secure and simple. PCA was used to extract facial data, as
well as training and testing. In which neural networks are
achieved, the system operates in several orientations. M.
Kalyani.K and Veera-Muthu proposed a 3D face recognition
method for the system for managing attendance, each
student’s attendance was recorded in the monthly
timeframe. There is an urgent need for a new algorithm
that can improve the identification of oriented faces. The
PCA algorithm was used to create an effective attendance
management system that attained a precision of up-to 83
percent, but the system’s performance was disturbed by
minute changes in lighting conditions. The author
introduced an eigenface technique along with PCA
algorithm for marking facial recognition attendance
systems, and they mentioned comparisons of various facial
recognition algorithms. Overall, keeping a record of
attendance was a good idea.
3. SYSTEM FEATURES
System features are highly divided into 3 modules.
 Registration and Access Module This module works
closely with related tasks For the registration of any
new user in the organization, Login to the system, and
managing user profile information. Using features
provided by this module manager may register new
users in the system and the controller/user can access
both the system through their credentials.
 Manage attendance information This module primarily
addresses issues concerning the student’s attendance.
Students can use it to record their presence, time of
entrance, and time of departure in the system. Admin
can view each student’s report generated by the
system, and students can view their attendance report,
With some filters, like filter by date and by student
name.
 Manage Student Records This module includes features
of the user profile. By using this module admin can add
a photo of the newly registered student during
registration. Admin can also command the system
explicitly to train the model and the system will make
necessary calculations and will generate some data
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022 www.irjet.net p-ISSN: 2395-0072

which will be used internally to identify each student
uniquely.
3.1.Manage Attendance and Login
Student registration
New Registrations can be made by the admin.
Input: Student information (username and password)
Output: Success message is displayed "user successfully
registered".
Login page
Input: Username and password
Output: If username and password are verified, the user
will be directed to the dashboard
Exception: If entered username and password are invalid
then the system will redirect the user to the login page, and
an error message is displayed.
3.2.Administer Attendance Info
Marking your presence-in
Input: With the use of an integrated webcam or external
webcam system will scan the student's face.
Output: Registered students will be identified and system
will mark students’ time of entrance to the database. A
success message will be transmitted to the user.
Mark your presence-out
Input: With the use of an integrated webcam or external
webcam system will scan the student's face.
Output: Registered students will be identified and the
system will mark the student’s time of entrance to the
database. A success message will be transmitted to the user.
View my attendance report
Students may often need to see their attendance record
throughout. the month or year. Using this feature students
can see their attendance record to the date.
Input: User selection
Output: Statistical analytics of the particular student who is
currently logged into the system will be displayed.
View student’s attendance report
This feature is for the admin for monitoring the availability
of each student to the date. i.e., how many students are
present today out of total students etc. can be monitored.
Input: user selection
Output: Attendance record of each student including how
many students are present today along with the graph.
3.3.Manage Student’s Details
Adding images
This feature can only be accessed by the admin, Admin can
capture images of students via webcam while the
registration process and create a folder with username as
the folder name in face_recognition_data.
Input: Student’s username
Output: Dataset created successfully
Training the system
Input: Username of student
Output: The system will train a machine learning model
with the available datasets created in the above process.
4. METHEDOLOGY
4.1.Data Acquisition
User Creation
The user is created with a specific username and
password.
Image Capture
Images are captured using the webcam of the laptop using
the OpenCV module.
Dataset Creation
Dataset is created to train the model. For better accuracy,
25 images of the student are captured. The captured image
is stored in a folder with names specific to the respected
student.
4.2.Facial Recognition
Face Detection and Feature Extraction
Facial recognition is a significant aspect wherein the image
captured by the camera is fed into the system, a facial
recognition algorithm is used to recognize the unique faces
in the input dataset, several computer vision algorithms
are being used to identify faces in an image, and the
location of facial landmarks is also determined. To identify
different faces within a given image, we have used the HOG
feature extraction algorithm.
Face Alignment
There are 68 distinct points on the person’s face or 68
landmarks. This step’s primary aim is to discover facial
landmarks and placement of faces in the image without
distorting the image.
Encoding of the Face
After detecting faces in an input dataset of faces, the
Extraction of uniquely identifying features from the face is
an important step. Essentially, whenever we get face

localization, the 128 specific facial features were extracted
from each image from the input dataset, and all these 128-
d facial features are saved in a 23 data file for face
recognition.
Face recognition
The final stage of the face detection and recognition
procedure. We used one of the most effective learning
techniques, deep metric learning, being highly accurate and
competent in producing real-value featureovectors. This
system validates the faces by building the 128-d embedding
for each one. The Euclidean distance between the face in
the image and all faces in the dataset is computed using an
internally compare-faces function. If the current image
matches the 70 percent threshold in the entire dataset, it
will proceed to attendance marking.
4.3.Marking Attendance
The system will recognize the user uniquely and will mark
the student’s time of departure in the database. Success
Message will be received by the user. The attendance
record of each student including the number of students
present today out of the total along with the graph will be
displayed.
5. RESULTS
Home Page
Smart Attendance Management System is effective and
efficient. On the home page, the user can mark his
attendance with options to mark the arrival and departure
time. The system works flawlessly after the registration
process of a student is completed by the admin.
Login
If the username and password are correct, the user will be
taken to the system’s dashboard. If the username and
password en?tered are invalid, the user will be routed to
the login page with an error notice.
Admin
Admin can perform following tasks:-
 Admin can register new students
 Add photos of students
 Train the model using dataset
 View attendance reports of all students
Register new student
Admin can register new students with unique username
and password.
Training Dataset
The system will train the machine learning model on the
available dataset of images of students. After training, the
system will be capable of identifying students distinctively.
Marking Attendance in
The system will uniquely identify the user and will mark
his/her arrival time in the database. A success message will
be received by the user.
Marking Attendance out
The system will uniquely identify the user and will mark
his/her departure time in the database. A success message
will be received by the user.

View Attendance report
Statistical analytics of the particular employee who is
currently logged into the system will be displayed.
About us
Description about the project, details of team members,
and contact form to reach out.
3. CONCLUSIONS
Attendance via facial recognition is a great replacement for
the traditional Attendance marking system (manual).
Concepts of facial recognition for marking attendance of
students and making the system more efficient. The system
can detect faces effectively with different positions and
performs efficiently. The system has some limitations
sometimes it fails to recognize faces from long distances,
and it is less optimized with a low configuration system,
but this can be resolved by the use of devices with high
computing power. In-short facial recognition is the future
of biometric security.
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