Đồ án tốt nghiệp - Real - time attendance management system based on face recognition with facial FT

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION
FACULTY FOR HIGH QUALITY TRAINING

CAPSTONE PROJECT

REAL – TIME ATTENDANCE MANAGEMENT
SYSTEM BASED ON FACE RECOGNITION WITH
FACIAL FEATURES TECHNIQUES
Student 1: NGUYEN DAC DUONG
Student ID: 18161006
Student 2: NGUYEN QUOC HUNG
Student ID: 18161018

Major: ELECTRONICS AND COMMUNICATIONS
ENGINEERING TECHNOLOGY
Advisor: PHAM NGOC SON, Dr.

Ho Chi Minh City, August 2022

THE SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom– Happiness

-Ho Chi Minh City, July 27, 2022

CAPSTONE PROJECT ASSIGNMENT
Student name: NGUYEN DAC DUONG

Student ID: 18161006

Student name: NGUYEN QUOC HUNG

Student ID: 18161018

Major: Electronics and Communications Engineering
Class: 18161CLA
Technology
Advisor: PHAM NGOC SON, Dr.
Phone number: 0966609555
Date of assignment: 06/03/2022

Date of submission: 20/07/2022

1. Project title: Attendance system using face recognition and face mask detection
2. Initial materials provided by the advisor:
Papers, book, regulations on ethics in scientific research.
3. Content of the project:
Develop a system using OpenCV and machine learning that automatically takes
attendance in class using facial recognition and converts data to an excel file for time
attendance.
4. Final product: Software
CHAIR OF THE PROGRAM

ADVISOR

(Sign with full name)

(Sign with full name)

THE SOCIALIST REPUBLIC OF VIETNAM
Independence – Freedom– Happiness

-Ho Chi Minh City, …, …, 2022

PRE-DEFENSE EVALUATION SHEET
Student name: NGUYEN DAC DUONG

Student ID: 18161006

Student name: NGUYEN QUOC HUNG

Student ID: 18161018

Major: Electronics and Communications Engineering Technology
Project title: Attendance system using face recognition and face mask detection
Name of Reviewer: .................................................................................................................
EVALUATION
1. Content and workload of the project
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2. Strengths:
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ani, K., and A. VeeraMuthu. “Smart application for AMS using face
recognition”, Computer Science &amp Engineering 3.5, 2013.
[11] Deshmukh, Badal J., and Sudhir M. Kharad. “Efficient Attendance Management: A
Face Recognition Approach”, International Journal of Computer Science Issues,
2014.
[12] Rachel Draelos, MD, Ph.D., “The History of Convolutional Neural Networks.
Glassboxmedicine. Retrieved December 26, 2020”, GLASS BOX, 2019, April 13.
https://glassboxmedicin-e.com/2019/04/13/ashorthistory-of-convolutional-neuralnetworks/.
[13] Deng L, Yu D. “Deep learning: methods and applications”, now Publishers Inc,
Foundations and Trends in Signal Processing 7, 2014.
58

[14] Bernhard Scholkopf and Alexander J Smola. “Learning with kernels: support vector
machines, regularization, optimization, and beyond”. MIT Press, 2001.
[15] Anil K Jain. “Data clustering: 50 years beyond K-means, Pattern recognition
letters”, Volume 31, Issue 8, ISSN 0167-8655, 2010.
[16] Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves.
“Asynchronous Methods for Deep Reinforcement Learning”. International
conference on machine learning, 2016.
[17] Bengio, Y. “Learning deep architectures for AI”. Foundations and Trends in Machine
Learning: Vol. 2: No. 1, pp 1-127, 15 Nov 2009. http://dx.doi.org/101561/2200000006
[18] Ajitesh Kumar, “Different Types of CNN Architectures Explained: Examples”, Data
Analytics, Powered by WordPress, 2022.
[19] J.K. Rahul Jayawardana, T. Sameera Bandaranayake, “Analysis of optimizing neural
networks and artificial intelligent Models for guidance, control, and navigation
systems”, International Research Journal of Modernization in Engineering
Technology and Science, 2021.
[20] W. Liu et al., “SSD: Single Shot MultiBox Detector”, ECCV 2016, Computer Vision
and Pattern Recognition, 2015. https://doi.org/10.48550/arXiv.1512.02325
[21] Srishilesh P S, “An Introduction to KNN Algorithm”, Slack community, 2021.
[22] Zhengyou Zhang, “A Survey of Recent Advances in Face Detection”, Microsoft,
2010.
[23] M. Xin and Y. Wang, “Research on image classification model based on deep
convolutional neural network” vol. 8, 2019.
[24] OpenCV team, “OpenCV”, Opencv.org. [Online]. Available: https://opencv.org/.
2020.
[25] Adrian Rosebrock, “COVID-19: Face Mask Detector
Keras/TensorFlow, and Deep Learning”, May 4, 2020.

with

OpenCV,

[26] Adrian Rosebrock, “Liveness Detection with OpenCV”, PyImageSearch. All Rights
Reserved, March 11, 2019.

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