Real-time deep learning-based face detection and recognition with integrated liveness detection for attendance system
This project presents the design and development of a real-time facial recognition attendance system aimed at automating and enhancing student attendance tracking in academic settings. Leveraging advancements in Artificial Intelligence and Computer Vision, the system integrates a deep learning-based...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7234/1/fyp_CS_2025_TYX.pdf http://eprints.utar.edu.my/7234/ |
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| Summary: | This project presents the design and development of a real-time facial recognition attendance system aimed at automating and enhancing student attendance tracking in academic settings. Leveraging advancements in Artificial Intelligence and Computer Vision, the system integrates a deep learning-based Convolutional Neural Network (CNN) that generates 1024-dimensional facial embeddings for each registered user. These embeddings are used for identity verification through cosine similarity matching, achieving reliable and high-accuracy face recognition. To address security vulnerabilities such as spoofing and proxy attendance, the system incorporates active liveness detection mechanisms, including blink detection and head movement analysis, ensuring that only live human faces are authenticated. The front-end interface enables students to register their facial data and perform attendance scanning with minimal user interaction, while the web-based backend dashboard allows lecturers to manage class sections, enroll students, and monitor attendance records. The overall system demonstrates robust performance in real-world scenarios, achieving face recognition high accuracy with consistently high precision, recall, and F1-score. SQLite is used for lightweight data storage, while the Flask framework supports the real-time backend operations. The modular architecture ensures extensibility for future improvements. While the prototype is effective for controlled environments, limitations such as dataset diversity, backend scalability, and mobile accessibility remain. Future work may focus on expanding dataset coverage, implementing a cross-platform mobile application, and upgrading to a cloud-based database for better scalability. Overall, this project serves as proof-of-concept for a secure, efficient, and deployable biometric attendance system that reduces manual effort and improves accountability in academic institutions. |
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