Multi-Person Automatic Attendance System Based on YOLOv11-Pose and FaceNet

This paper proposes a real-time automatic attendance system that combines pose estimation and face recognition to operate without any manual input. The system uses the YOLOv11-Pose model to detect hand-raising gestures, which serve as an explicit signal of attendance intent. Only individuals detected with raised hands are passed to the face recognition module, thereby reducing unnecessary computation. The pose estimation is performed based on the relative position of the wrist and shoulder keypoints, allowing robust detection even in multi-person classroom environments. For identity verification, a FaceNet-based embedding model is fine-tuned using Supervised Contrastive Learning (SupCon) to better reflect East Asian facial characteristics. This approach improves intra-class compactness and inter-class separability in the embedding space. Experimental evaluations confirm that the proposed system achieves high precision in gesture detection and improved accuracy in face recognition, showing practical applicability for automated attendance tracking in real-world educational settings.