Đồ án tốt nghiệp - Design and implementation of a smart attendance system applying facial R

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION
FACULTY OF INTERNATIONAL EDUCATION

CAPSTONE PROJECT

DESIGN AND IMPLEMENTATION OF A SMART
ATTENDANCE SYSTEM APPLYING FACIAL
RECOGNITION

Students: NGUYỄN GIA HƯNG
ID: 19119052
NGUYỄN ĐÌNH HỒNG QUÂN
ID: 19119045

Major: COMPUTER ENGINEERING TECHNOLOGY
Advisor: M.E. LÊ MINH

Ho Chi Minh City, Jan 2024

THE SOCIALIST REPUBLIC OF VIETNAM

Independence – Freedom– Happiness
-Ho Chi Minh City, January

, 2024

GRADUATION PROJECT ASSIGNMENT
Student name: _________________________ Student ID: ___________________
Student name: __________________________ Student ID: ___________________
Student name: __________________________ Student ID: ___________________
Major: ________________________________ Class: ________________________
Supervisor: ____________________________ Phone number: _________________
Date of assignment: _____________________ Date of submission: _____________

1. Project title: _________________________________________________________
2. Initial materials provided by supervisor: ___________________________________
3. Content of the project: _________________________________________________
4. Final product: ________________________________________________________
CHAIR OF THE PROGRAM

SUPERVISOR

(Sign with full name)

(Sign with full name)

THE SOCIALIST REPUBLIC OF VIETNAM

Independence – Freedom– Happiness


PRE-DEFENSE EVALUATION SHEET
Student name: ................................................................. Student ID: .............................
Student name: ................................................................. Student ID: .............................
Student name: ................................................................. Student ID: .............................
Major: .....................................................................................................................................
Project title: .............................................................................................................................
.................................................................................................................................................
Name of Examiner: .................................................................................................................

EVALUATION
1. Content and workload of the project
...........................................................
...

...
ure recognition to determine the level of similarity between images,
reducing the risk of deception through impersonation methods. Integrate gesture
recognition algorithms to verify the validity of user actions.
-

Accuracy and Reliability: Increase accuracy and reliability, the detection model in

the system needs to be trained with a better dataset such as many images with low
light quality, or images contain the users face in some special cases such as wearing
clothes. masks, glasses,...
-

Cost and Maintenance: Optimize system costs and maintenance, we need to

provide good enough hardware for the system at a price range like Nvidia Jetson
Nano Developer Kit B01. This is good enough hardware to process images for the AI
model, and is also suitable for small and medium-sized systems.
-

Distance: Model detection on the system requires a lot of data with different

distances of the users face from the webcam.

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REFERENCES
[1]. Youshan Zhang (2023, Dec. 13). CowStallNumbers: A Small Dataset for Stall
Number Detection of Cow Teats Images [Online]. Available: (PDF) Stall Number
Detection of Cow Teats Key Frames (researchgate.net)
[2]. Rohit Kundu (2023, Dec. 14). YOLO: Algorithm for Object Detection Explained
[Online]. Available: YOLO Algorithm for Object Detection Explained [+Examples]
(v7labs.com)
[3]. Google (2023, Sep. 16). Hand landmarks detection guide [Online]. Available:
https://developers.google.com/mediapipe/solutions/vision/hand_landmarker
[4]. Jacob Solawetz and Francesco (2023, Dec 14). What is YOLOv8? The Ultimate
Guide [Online]. Available: What is YOLOv8? The Ultimate Guide. (roboflow.com)
[5]. Denis Kuria (2023, Sep. 25). Yolov8 How train custom data [online]. Available:
How to Train YOLOv8 on Custom Data (makeuseof.com)
[6]. Ultralytics (2023, Sep.15). Yolo network [Online]. Available: Train - Ultralytics
YOLOv8 Docs
[7]. Sunny Kumar (2023, Sep. 17). Hand tracking using Mediapipe [Online]. Available:
Hand tracking using Mediapipe. Access full tutorial of body pose… | by Sunny
Kumar | Medium
[8]. Suchandra Datta (2023, Oct. 10). Firebase with Python [Online]. Available: How to
Get Started with Firebase Using Python (freecodecamp.org)
[9]. Logitech (2023, Oct. 13). C270-HD-Webcam [Online]. Available:
https://www.logitech.com/vi-vn/products/webcams/c270-hd-webcam.960000584.html
[10]. Cytron (2023, Oct. 13). Raspberry Pi4 Model B 8Gb Ram [Online]. Available:
cytrontech.vn/tutorial/raspberry-pi-4-model-B-8GB-RAM

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