AI-based Efficient Hallway Attendance Monitoring using Confidence-driven Face Recognition

Attendance monitoring deployed at entrance areas offers reliable tracking for institutional environments. We propose an efficient face-recognition-based framework combining YOLOv11-based face detection, motion-based tracking, and FaceNet-based recognition. Our key contribution is a confidence-driven face selection method that collects multiple face images per track and selects the highest-quality image for recognition. Experiments on real-world 60-second hallway footage (1,800 frames at 30 FPS) demonstrated up to 77.7% improvement in processing speed (79.36 FPS vs. 44.66 FPS baseline) while maintaining equivalent recognition accuracy. Collection windows of 5 to 7 images provide optimal balance between latency and throughput (73.67–75.86 FPS), demonstrating that strategic recognition frequency reduction enables real-time attendance monitoring suitable for resource-constrained environments