Face recognition models improve classroom attendance accuracy
Towards Robust Classroom Attendance: A Comprehensive Evaluation of Face Detection and Recognition Models
Computer Vision and Pattern RecognitionMachine Learning
Summary
Taking attendance in classrooms can be slow and prone to mistakes when done manually. The authors created a new set of student face photos taken under real classroom conditions to help improve face recognition accuracy. They tested several face recognition models and found that one called FaceLiVTv2-M works best on this challenging data. This shows their new photo set is useful for improving attendance systems in schools using face recognition.
What this means in practice
- •For school it administrators: Develop automated attendance systems that accurately track student presence under varied classroom conditions using the tested face recognition models.
- •For security system developers: Build more robust face detection and recognition software that performs well in environments with varied lighting and partial face coverage using the Visage Face dataset.
Authors
Himani Trivedi, Hiren Patel, Ridham Patel, Krutika Patel, Nancy Patel
Abstract
Manual attendance methods, such as paper or register-based systems, take a lot of time, can lead to errors, and are easy to falsify. Face recognition is more reliable, but it frequently struggles in classrooms because lighting and other conditions can vary. Face recognition datasets are designed for regulated environments and do not capture the actual challenges found in classrooms. To address this, a new face detection and recognition dataset, the Visage Face dataset, comprising 16,234 face samples, is proposed for the task of face detection and recognition. The photos are taken from different angles and under varying lighting conditions, with students showing a range of expressions, and some faces partly covered to reflect real-life situations. A YOLO-based system is used to detect faces and tested seven advanced face recognition models with thirteen configurations: LVFace, QCFace, FaceLiVTv2, TopoFR, EdgeFace, TransFace, and GhostFaceNets. Of these, FaceLiVTv2-M performed best, with 99.75% Top-1/Top-5 accuracy and an inference time of 6.459 ms. These results show that the Visage Face Dataset is a realistic and challenging benchmark for face recognition in classroom attendance.