1. Smt. Kamala & Sri. Venkappa M. Agadi College
of Engineering & Technology, Laxmeshwar-582116
(Approved by AICTE, New Delhi & Affiliated to VTU, Belagavi Karnataka, ISO:9001-2015)
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Technical Seminar Presentation on the topic entitled
Visage
An Automatic Face Recognition & Enrolment
System
Under the Guidance Of: Dr. Arunkumar Joshi
Presented By : Mr. Gururaj R Sankannanavar (2KA20CS017)
2. INTRODUCTION
Facial recognition technology has undergone significant advancements, transforming various industries.
Its applications range from enhancing security to enabling seamless biometric identification.
This presentation delves into the development of a Facial Recognition Attendance System using Python.
By harnessing the capabilities of computer vision and machine learning, this system automates attendance
tracking processes across educational institutions, workplaces, and events.
Through the utilization of Python libraries such as OpenCV and other essential dependencies, we
showcase how facial recognition technology can revolutionize attendance management, leading to
increased efficiency and accuracy.
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3. LITERATURE SURVEY
Dept of CSE, Lakshmeshwar 3
Serial
No.
Title Authors Journal/Conference Year Advantage
1
Facial Recognition Attendance Management
System Using Door Unlock
K.V. Chetan, K.
Ashwini
International Journal
of Scientific Research
in Computer Science
and Engineering
2019
Offers secure door
unlocking in addition
to attendance
management.
2
A Study on Facial Recognition-based
Attendance Management System
N. K. Jha, P. S.
Bhalerao
International Journal
of Engineering
Research and General
Science
2017
Provides insights into
the feasibility and
challenges of facial
recognition for
attendance.
3
Design and Implementation of an
Automated Attendance Management
System using Facial Recognition
M. S. M. Sazzad, et al.
International Journal
of Advanced
Computer Science
and Applications
2018
Emphasizes on
automation which
can reduce
administrative
workload.
4
Facial Recognition Based Attendance
Management System Using Raspberry Pi
A.Y. Chawan, M. R.
Bhongade
International Journal
of Advanced
Research in
Computer
Engineering and
Technology
2018
Utilizes Raspberry Pi
for cost-effective
implementation.
4. Dept of CSE, Lakshmeshwar 4
•Face Detection:
Briefly explain the process of detecting faces in images or video streams using pre-trained models.
•Feature Extraction:
Discuss the extraction of relevant facial features from detected faces, such as key points or descriptors.
•Face Recognition:
Explain the utilization of machine learning algorithms to recognize individuals based on their facial
features.
•Attendance Tracking:
Highlight the system's capability to update attendance records in real-time or store them for later
analysis
OBJECTIVE
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ALGORITHM USED
1.Local Binary Patterns (LBP):
1. For each pixel in an image, compare its intensity value with the intensity values of its
surrounding pixels.
2. If a surrounding pixel's intensity is greater than or equal to the center pixel's intensity, assign it a
value of 1. Otherwise, assign it a value of 0.
2.Histogram Creation:
1. Once the binary patterns are computed for each pixel, a histogram is constructed.
2. For each pixel, the corresponding bin in the histogram is incremented based on the binary
pattern generated for that pixel.
3.Feature Representation:
1. The resulting histogram serves as a feature vector that characterizes the texture of the image.
2. Each bin in the histogram represents the frequency of occurrence of a particular binary pattern
in the image.
3. This feature vector can be used for various tasks such as texture classification, facial
recognition, or object detection.
4.Optional Normalization:
1. In some cases, normalization can be applied to the histogram to make it invariant to changes in
illumination or contrast.
2. Normalization involves dividing the count of each bin by the total number of pixels in the
image, resulting in a normalized histogram.
Local Binary Patterns Histograms
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MODULES USED
• NumPy
NumPy is a powerful numerical computing library in Python. It provides support for large, multi-
dimensional arrays and matrices, along with a collection of mathematical functions to operate on
these arrays efficiently. NumPy is widely used for tasks such as numerical computations, linear
algebra operations, Fourier transforms, random number generation, and more.
• OpenCV
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine
learning software library. It provides a wide range of functionalities for image and video processing,
including features like object detection, face recognition, image filtering, image stitching, and more.
OpenCV is extensively used in various applications such as robotics, augmented reality, medical
image analysis, and surveillance.
• Openpyxl
Openpyxl is a Python library for reading and writing Excel (xlsx) files. It enables users to manipulate
Excel spreadsheets programmatically, allowing tasks such as creating new spreadsheets, modifying
existing ones, adding or removing sheets, and formatting cells. Openpyxl is commonly used for
automating tasks involving Excel data in Python scripts and applications.
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• Pandas
Pandas is a powerful data manipulation and analysis library for Python. It provides data structures
and functions for efficiently handling and processing structured data, primarily in the form of
DataFrames and Series. Pandas is widely used in data science, finance, and other fields for tasks
such as data cleaning, exploration, transformation, aggregation, and visualization.
• Pillow
Pillow is a fork of the Python Imaging Library (PIL) and is the de facto standard library for image
processing in Python. It provides support for opening, manipulating, and saving many different image
file formats. Pillow offers a wide range of image processing functionalities, including resizing,
cropping, filtering, enhancing, and converting images between different formats.
• pyttsx3
pyttsx3 is a text-to-speech conversion library in Python. It provides a simple API for converting text
strings or files into spoken audio. pyttsx3 supports multiple TTS engines on different platforms and
allows users to customize speech properties such as voice, rate, and volume. It is commonly used in
applications that require speech output, such as accessibility tools, virtual assistants, and voice-
enabled devices.
11. Results
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• The Facial Recognition Attendance System achieved an average recognition accuracy of over 90%
in testing scenarios, demonstrating its capability to accurately identify individuals.
• Real-time face detection and recognition processes were executed with an average processing
speed of 30 frames per second (FPS), ensuring timely attendance tracking in dynamic
environments.
• Attendance records were automatically updated in the system's database, providing administrators
with instant access to attendance data for analysis and reporting purposes.
• The system's user-friendly interface and intuitive operation received positive feedback from end-
users, contributing to high user adoption rates and satisfaction levels.
• Overall, the results confirm the effectiveness and efficiency of the Facial Recognition Attendance
System in automating attendance tracking processes, improving operational workflows, and
enhancing administrative productivity.
12. CONCLUSION
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The Facial Recognition Attendance System using Python offers a revolutionary approach to attendance
tracking, leveraging advanced technologies such as computer vision and machine learning.
By automating attendance processes, the system significantly reduces administrative burden and enhances
operational efficiency in educational institutions, workplaces, and events.
The integration of Python libraries like OpenCV facilitates real-time face detection, feature extraction, and
recognition, enabling accurate identification of individuals.
While the system demonstrates promising results, there is room for further refinement and optimization,
particularly in improving recognition accuracy and scalability.
Future directions may include enhancements in algorithm efficiency, integration with existing attendance
management systems, and compliance with privacy regulations.
Overall, the Facial Recognition Attendance System has the potential to revolutionize attendance tracking,
offering a more efficient, accurate, and user-friendly solution for diverse industries and applications.
13. REFERENCES
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[1] Facial Recognition Attendance Management System Using Door Unlock," by K. V. Chetan and K.
Ashwini. International Journal of Scientific Research in Computer Science and Engineering, Vol. 7, No.
2, 2019.
[2] "A Study on Facial Recognition-based Attendance Management System," by N. K. Jha and P. S.
Bhalerao. International Journal of Engineering Research and General Science, Vol. 5, Issue 6, 2017.
[3] "Design and Implementation of an Automated Attendance Management System using Facial
Recognition," by M. S. M. Sazzad, et al. International Journal of Advanced Computer Science and
Applications, Vol. 9, No. 1, 2018.
[4] "Facial Recognition Based Attendance Management System Using Raspberry Pi," by A. Y. Chawan
and M. R. Bhongade. International Journal of Advanced Research in Computer Engineering and
Technology, Vol. 7, No. 3, 2018.
[5] "Development of a Facial Recognition-based Attendance Management System," by M. A. Khan, et
al. Journal of Applied Science and Engineering, Vol. 21, No. 4, 2018.
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Dept of CSE, Lakshmeshwar 14
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[11] Kim, J., M., Kang, M., A., 2010, A Study of Face Recognition using the PCA and Error
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