Towards Robust Classroom Attendance: A Comprehensive Evaluation of Face Detection and Recognition Models

The paper introduces the Visage Face dataset, containing 16,234 face samples captured in varied classroom conditions such as different angles, lighting, expressions, and partial occlusions. Using a YOLO-based detector, the authors evaluate seven state‑of‑the‑art face recognition models across thirteen configurations, finding that FaceLiVTv2‑M achieves the highest performance with 99.75% Top‑1/Top‑5 accuracy and a 6.459 ms inference time. These findings demonstrate that the Visage Face dataset provides a realistic and challenging benchmark for classroom attendance systems.

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• computer-vision
• benchmarks