📰 01 paper is accepted at The 18th IEEE-RIVF International Conference on Computing & Communication Technologies (RIVF 2024), Da Nang, Vietnam.

Our paper titled "Improving Face Attendance Checking System with Ensemble Learning" has been accepted for presentation at the 18th IEEE-RIVF International Conference on Computing & Communication Technologies (RIVF 2024) in Da Nang, Vietnam. This work proposes a robust face attendance system that integrates multiple deep learning models—ResNet, VGGFace, and FaceNet—through a weighted ensemble learning approach. By combining the complementary strengths of these models, the system achieves up to 100% accuracy in experiments using the LFW dataset under optimal conditions. The framework incorporates efficient face detection (MTCNN, Dlib, YOLO5Face), class-based filtering to reduce computation, and a two-phase workflow for database preparation and inference. Our findings demonstrate that ensemble learning can significantly enhance the accuracy, robustness, and practicality of face attendance systems in real-world educational and workplace environments.

• Duc Tai Phan, Phuong-Nam Tran, and Dr. Duc Ngoc Minh Dang, “Improving Face Attendance Checking System with Ensemble Learning”, The 18th IEEE-RIVF International Conference on Computing & Communication Technologies (RIVF 2024), Da Nang, Vietnam