迈向稳健的课堂考勤：人脸检测与识别模型的综合评估
Towards Robust Classroom Attendance: A Comprehensive Evaluation of Face Detection and Recognition Models
- LDRP-ITR, Kadi Sarva Vishwavidyalaya（卡迪·萨尔瓦·维什瓦维迪亚雅拉 LDRP-ITR）
- VSITR, Kadi Sarva Vishwavidyalaya（卡迪·萨尔瓦·维什瓦维迪亚雅拉 VSITR）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对课堂考勤中人工方法低效及现有数据集不匹配问题，提出含16,234样本的Visage Face数据集，并评估七种识别模型，其中FaceLiVTv2-M以99.75%准确率表现最佳。
AI中文摘要：
人工考勤方法，如纸质或基于登记簿的系统，耗时较长，容易出错，且易于伪造。人脸识别更为可靠，但在教室环境中，由于光照和其他条件的变化，其性能常常受到影响。现有的人脸识别数据集是为受控环境设计的，未能反映教室中的实际挑战。为解决这一问题，本文提出一个新的人脸检测与识别数据集——Visage Face数据集，包含16,234个人脸样本，用于人脸检测与识别任务。照片从不同角度、在不同光照条件下拍摄，学生呈现多种表情，部分人脸被部分遮挡，以反映真实场景。系统采用基于YOLO的方法进行人脸检测，并测试了七种先进的人脸识别模型及其十三种配置：LVFace、QCFace、FaceLiVTv2、TopoFR、EdgeFace、TransFace和GhostFaceNets。其中，FaceLiVTv2-M表现最佳，Top-1/Top-5准确率达99.75%，推理时间为6.459毫秒。这些结果表明，Visage Face数据集是课堂考勤中人脸识别的现实且具有挑战性的基准。
英文摘要：
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.
