Title: "Intelligent Attendance System: Combining Fusion Setting with Robust Similarity Measure for Face Recognition"
Authors:
- Che Quang Huy – 18520819 – KTMT2018 – Main Author.
- Le Huu Truyen – 20522094 – KTMT2020 – Co-author.
- Ngo Man Dat – 19521333 – MTCL2019 – Co-author.
Supervisors:
- M.S. Phan Dinh Duy
- M.S. Tran Hoang Loc
Abstract:
The attendance and timekeeping system based on facial recognition is increasingly being widely applied in classrooms or businesses. However, challenges such as prediction accuracy, effective data collection methods, and limited hardware processing time (Embedded Computer) still exist when implementing attendance systems in real-world scenarios. Most recognition methods compare specific features individually, making it impractical to use combined features for simultaneous face recognition. This paper presents an attendance system using facial recognition technology. To enhance facial recognition accuracy, intelligent data collection techniques and improvements in face detection are applied. A clustering algorithm is utilized to select relevant facial data for feature comparison in the intelligent data collection process. By optimizing face detection accuracy, we combine results across multiple frames and introduce a new method for information retrieval by combining centroid-based and instance-based distance. Logistic regression is used to determine weights for this method. We evaluate our combined method on the VN-celeb dataset, demonstrating superior accuracy (92.63% with Arcface Resnet101) compared to centroid-based (91.97%) and instance-based (91.67%) methods. Furthermore, we extend the application of our proposed attendance system to an intelligent attendance system, utilizing hardware components such as Raspberry Pi 4 and additional devices. This system is supported by infrastructure databases and a user-friendly web interface, allowing users to conveniently view attendance-related information.
"We sincerely thank Mr. Phan Dinh Duy – Associate Dean of the Computer Science Faculty and Mr. Tran Hoang Loc – Secretary of the Computer Science Faculty’s HCYU, as well as the CEEC club, for their support in creating favorable conditions for our research. Once again, we express our gratitude to both supervisors for their support, exchange, and feedback, enabling us to complete this research project to the best of our ability."
The "International Conference on Multimedia Analysis and Pattern Recognition - MAPR" is an annual international conference co-founded and organized by the University of Information Technology (UIT). This Scopus-Indexed international conference aims to promote the development of science and technology in the fields of Artificial Intelligence and Machine Learning. The conference gathers researchers and experts from both academia and industry to share the latest research results, test findings, and strengthen potential cooperation opportunities in pattern recognition, multimedia analysis, and related fields. Some topics of interest include Pattern Recognition and Machine Learning, Multimedia Analysis, Biomedical Image Analysis and Biometrics, Computer Vision and Robot Vision, Document Analysis and Recognition, Applications, etc. The 6th International Scientific Conference MAPR 2023 is organized by the University of Information Technology (UIT), in collaboration with VAPR (Vietnamese Association on Pattern Recognition), Hanoi University of Science and Technology (HUST), and Institute of Information Technology (IOIT). The conference took place in Quy Nhon, Binh Dinh, on October 5 and 6, 2023. The conference attracted many scientists, doctoral students, graduate students, and students from various countries, including the United States, France, Japan, Malaysia, and Vietnam. With its papers published on IEEE Xplore and indexed in Scopus, MAPR conference has achieved certain success in promoting the development of multimedia analysis and recognition in Vietnam and worldwide.
For more details, visit: https://www.facebook.com/UIT.Fanpage/posts/pfbid02vfC8wv8JY2w...
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