Facial Recognition based Attendance System: A Survey

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Facial Recognition based Attendance System: A Survey
1,2,3,4B. Tech Student, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India
Abstract - Efficient attendance monitoring is hindered by
time-consuming manual methods vulnerable to inaccuracies
and fraud. The Attendance System with Face Recognition
offers a pioneering solution, diverging from traditional
approaches. Utilizing advanced facial recognition
technology, it ensures precise identification and verification,
minimizing errors. With a user-friendly interface, it enhances
accessibility for administrators and end-users. Real-time
tracking empowers swift issue resolution, improving
operational efficiency and data integrity. Representing a
paradigm shift in attendance management, this system
provides a secure, accurate, and efficient alternative to
conventional methods in educational institutions, corporate
offices, and organizations.
Key Words: Facial Recognition Technology, Real-time
Tracking, OpenCV, InsightFace, Cosine Similarity, Machine
Learning
1.INTRODUCTION
The Attendance System with Face Recognition represents a
technological leap forward in the realm of attendance
tracking and management. Harnessing the power of
cutting-edge facial recognition technology, this system
offers a seamless and highly efficient solution for
accurately monitoring attendance in various settings,
including educational institutions, corporate offices, and
organizations. Traditional attendance methods, often
plagued by errors and inefficiencies, are eclipsed by this
innovative system's ability to identify and verify individuals
through their unique facial features. With real-time
monitoring, robust security measures, and the flexibility to
integrate with existing databases, the Attendance System
with Face Recognition not only simplifies the attendance
tracking process but also ensures precision, security, and
compliance with data protection regulations. It is a
transformative tool that has the potential to streamline
operations and elevate the quality of attendance
management in today's fastpaced and data-driven world.
1.1 Need for Facial Recognition in Attendance System
Additionally, real-time monitoring and instant data
updates empower administrators to make prompt
decisions and interventions when needed, further
enhancing operational efficiency. The integration of face
recognition technology in attendance systems not only
offers a streamlined and secure approach to tracking
attendance but also represents a forward-thinking solution
that aligns with the demands of modern organizations and
educational institutions.
1.2 Purpose
An Attendance System with Face Recognition built using
OpenCV, Machine Learning, and Python, embodies the
convergence of advanced technologies to provide a
cuttingedge solution for attendance tracking. OpenCV, a
powerful computer vision library, forms the backbone of
this system, enabling it to capture, analyse, and recognize
faces in realtime. Through the utilization of Machine
Learning algorithms, the system learns to identify and
differentiate individuals based on their facial features.
Python serves as the programming language that
orchestrates these technologies, facilitating seamless
integration and customization.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 11 | Nov 2023 www.irjet.net p-ISSN: 2395-0072
The use of facial recognition technology into attendance
systems has transformed the way businesses and
institutions measure attendance. By leveraging the unique
and unalterable facial features of individuals, this
technology ensures a high degree of accuracy and security
in the attendance management process. When individuals
interact with a face recognition-enabled system, their facial
data is captured and compared to a database of stored
facial templates, allowing for quick and precise
identification. This technology not only eliminates the need
for manual data entry but also mitigates the risks
associated with proxy attendance, a common issue in
traditional methods.
2. LITERATURE REVIEW
2.1 Facial Recognition
Facial recognition attendance systems have emerged as a
game changer in the field of employee monitoring and
attendance management. Leveraging the power of machine
learning and Python, these systems offer a seamless and
efficient approach to tracking employee presence.
The implementation of facial recognition attendance
systems typically involves three key stages: face detection,
feature extraction, and recognition. Face detection
algorithms identify and locate faces within images or video
Mihir Ghanekar1, Archita Sehgal2, Shrishail Gouragond3, Maitray Wani4, Prof. Pramila M.
Chawan5
5Associate Professor, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India
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2.2 Machine Learning Search Algorithms
Machine learning powers facial recognition attendance
systems, enabling accurate and efficient identification
through facial feature recognition. Trained on vast datasets,
these systems adapt to changes in appearance, ensuring
consistent performance.
2.2.1 Manhattan Distance
In Facial Recognition Attendance Systems, Manhattan
Distance, a crucial metric, measures the likeness between
captured and stored facial templates. It quantifies
differences by summing absolute feature disparities, aiding
swift and accurate identification. This enhances security
and ensures reliable attendance tracking.
2.2.2 Euclidean Distance
Euclidean Distance is a critical tool employed by machine
learning algorithms to determine the similarity between a
captured facial template and those stored in the database.
It calculates the straight-line distance between two sets of
facial features in a multidimensional space, providing a
measure of their likeness. Euclidean Distance enhances the
system's ability to identify individuals accurately and
quickly. By using this metric, the system can effectively
match the facial data, ensuring precise attendance tracking
and bolstering security in the process.
2.2.3 Chebyshev Distance
Chebyshev Distance plays a crucial role as a distance
metric used by machine learning algorithms to assess the
similarity between a captured facial template and those
stored in the system's database. It calculates the maximum
absolute difference between corresponding features in the
two sets, providing a robust measure of likeness.
Chebyshev Distance aids in quick and accurate individual
identification, contributing to the system's precision and
efficiency.
2.2.4 Minkowski Distance
Minkowski Distances are an adaptable collection of
distance metrics that include Manhattan, Euclidean, and
Chebyshev distances, providing for greater versatility in
assessing resemblance. This adaptability enables the
system to fine-tune the matching process to suit specific
identification needs, ensuring both precision and efficiency.
2.2.5 Cosine Similarity
2.2.6 Distance Method
Distance Method refers to the use of specific distance
metrics, such as Euclidean, Manhattan, or Chebyshev
distances, to measure the likeness between a captured
facial template and those stored in the system's database.
These metrics calculate the difference between
corresponding facial features, offering a quantifiable
measure of similarity. By employing the Distance Method,
the system efficiently matches and verifies individuals,
contributing to precise attendance tracking.
2.2.7 Similarity Method
This method relies on machine learning to compare
the facial templates of individuals in real-time with those
stored in the database. It measures the similarity between
the captured facial features and the stored templates,
allowing for quick and accurate identification. The
algorithm enhances the system's efficiency, enabling it to
recognize individuals promptly, while minimizing false
positives and negatives. As a result, the Similarity Method
is an essential part of ensuring that attendance records are
both secure and precise.
streams. Feature extraction techniques then extract unique
facial features, such as the shape of the nose, eyes, and lips.
Finally, recognition algorithms compare the extracted
features against a database of known faces to identify the
individual.
Cosine Similarity computes the cosine of the angle
between these templates to determine their similarity.
Cosine Similarity enhances the system's ability to identify
individuals accurately, particularly in scenarios where
facial appearance may vary due to different lighting or
angles.
Search
Algorithm
Key Difference Accuracy
rate
Manhattan
Distance
Sum of absolute
differences between
corresponding
coordinates.
85%
Chebyshev
Distance
Maximum absolute
difference between
corresponding
coordinates
80%
Minkowski
Distance
Generalization of both
Manhattan and
Chebyshev distances.
82%
Distance
Method
Measures dissimilarity
between feature vectors.
78%
Similarity
Method
Measures similarity
between feature vectors.
79%
Cosine
Similarity
Cosine Similarity
calculates the angle's
cosine between two non-
zero vectors. Often used
for high-dimensional
data like facial features.
Closer to 1 means more
similarity.
92%
Table -1: Comparison of different Search algorithms.

Cosine Similarity stands out as the preferred choice in
facial recognition due to its ability to measure likeness
between facial templates, considering variations in lighting
and angles. Unlike distance metrics such as Manhattan,
Euclidean, and Chebyshev, Cosine Similarity focuses on the
angle between vectors, making it robust to differences in
facial appearance. The Similarity Method, utilizing Cosine
Similarity, excels in accurately identifying individuals,
minimizing false positives and negatives. Its adaptability to
various facial features contributes to a higher accuracy rate
compared to other distance methods like Minkowski.
In facial recognition scenarios where precise
measurements of similarity are crucial, Cosine Similarity
emerges as the optimal choice, ensuring a more reliable
and efficient attendance tracking process with enhanced
security and data integrity.
Fig -1: Cosine Similarity in Face recognition.
3. PROPOSED SYSTEM
3.1 PROBLEM STATEMENT
Develop an efficient and secure Attendance System with
Face Recognition to overcome the shortcomings of
traditional attendance tracking in educational institutions
and organizations. The challenge involves leveraging
cutting-edge technologies like facial recognition, machine
learning, and database management to ensure real-time
monitoring, prevent proxy attendance, and enhance data
integrity. The goal is to create a user-friendly solution that
not only improves attendance accuracy but also prioritizes
privacy, seamless database integration, and insightful data
analysis, addressing the evolving needs of the modern
digital era.
3.2 PROBLEM ELABORATION
Furthermore, the automatic student attendance system
based on facial recognition can overcome the problem of
fraudulent approaches, and lecturers are not required to
count the number of students numerous times to confirm
the students' presence.
The traditional student attendance marking system
frequently faces difficulties. By eliminating traditional
student attendance marking techniques such as calling
student names or verifying relevant identity cards, the
facial recognition student attendance system highlights its
simplicity. They not only disrupt the instructional process,
but they also distract pupils during test sessions. During
the lecture sessions, an attendance sheet is handed around
the classroom in addition to calling names. It may be tough
to circulate the attendance sheet around the lecture class,
especially if there are a high number of students. Thus, a
face recognition attendance system is proposed to replace
the tedious manual signing of students' presence, which
causes students to get distracted to sign for their
attendance.
3.3 PROPOSED METHODOLOGY
We propose a clustering method of recommendation
systems. Clustering methods are an important tool in
machine learning and data analysis, and can be particularly
useful for mutual fund rec The proposed methodology for
developing a comprehensive Attendance System with Face
Recognition, integrating Redis database, OpenCV, machine
learning, and search algorithms in Python, involves a step-
by-step approach:
a) Data Collection and Annotation: Collect and
annotate a diverse facial image dataset for system
users.
b) Data Preprocessing: Apply normalization, resizing,
and noise reduction techniques for consistent and
quality images.
c) Feature Extraction and Template Creation: Use
OpenCV and machine learning to extract unique
facial features and create templates.
d) Machine Learning Model Development: Train a
model for accurate facial feature recognition,
optimizing for various conditions.
e) Redis Database Integration: Integrate Redis for
secure storage and retrieval of facial templates and
attendance records.
f) Search Algorithms and Matching: Implement
optimized search algorithms like Euclidean,
Manhattan, Chebyshev distances, or cosine
similarity.
g) Real-Time Monitoring and Alerting: Enable real-
time attendance monitoring and implement
alerting for immediate administrator action.
h) Customization and Integration: Design a highly
customizable system for tailored attendance
policies and seamless integration.
i) User Interface and Reporting: Develop a user
friendly Python interface and robust reporting
features for valuable insights.

j) Security and Data Protection: Address security and
privacy concerns through encryption, compliance,
and regular audits.
k) Real-World Testing and Validation: Conduct
comprehensive testing in real-world educational
and corporate environments.
l) Maintenance and Optimization: Develop a strategy
for continuous maintenance, updates, and
optimization for long-term reliability.
3.4 SYSTEM ARCHITECTURE
Fig -2: Proposed workflow
The above workflow has two planned phases. The first
phase is going to be about the Facial Recognition System
comprising of the Feature extraction and Algorithm
selection activities. This will be followed by creation of the
New User’s registration form and setting up the Redis
Database.
The second phase deals with creation of the two different
Streamlit Web Apps followed by compiling of the report
and dashboard.
5. CONCLUSION
The proposed Attendance System with Face
Recognition, incorporating Redis database, OpenCV,
machine learning, and search algorithms in Python,
presents a transformative solution with numerous
advantages across diverse domains. Notable benefits
include enhanced accuracy through facial recognition,
automation for increased efficiency, heightened security
against unauthorized access, and robust data integrity
provided by the Redis database. The system's
customization, user-friendly interface, and reporting
features make it adaptable to the unique needs of
educational institutions, corporate offices, and various
businesses. Its scalability and potential for cost savings
underscore its practicality for both small and large entities.
The system's versatile use cases span educational
institutions, corporate offices, healthcare facilities,
government institutions, retail outlets, transportation,
hospitality, and beyond.
REFERENCES
[1] P. Hegde, "face recognition based attendance
management system", International Journal of
Engineering Research
[2] Face Recognition Based Attendance System. (2020,
June). Dhanush Gowda H.L , K Vishal , Keertiraj B. R ,
Neha
[3] Hasan, R. and Sallow, A. (2021). face detection and
recognition using opencv. Journal of Soft Computing
and Data Mining, 2(2).
https://doi.org/10.30880/jscdm.2021.02.02.008
[4] Nath, R., Kakoty, K., Bora, D. J., & Welipitiya, U. (2021,
January 31). Face Detection and Recognition Using
Machine Learning. ResearchGate;
https://www.researchgate.net/publication/3489172
90_Face_Detection_and_Recognition_Using_Machine_L
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[5] Anjeana, N., & Anusudha, K. (2023, September 19).
Real time face recognition system based on YOLO and
InsightFace. Multimedia Tools and Applications;
Springer Science+Business Media.
https://doi.org/10.1007/s11042-023-16831-7
[6] Kulkarni. (2023, July). FACE RECOGNITION-BASED
ATTENDANCE MANAGEMENT SYSTEM. European
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[7] (n.d.). Face Recognition and Identification using Deep
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https://iopscience.iop.org/article/10.1088/17426596
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