发表机构
Yeshiva University（叶史瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对传统考勤耗时耗力的问题，提出ClassVision系统，采用RetinaFace结合人脸识别，通过实时图像处理实现教室考勤自动化，取得良好效果。
AI 中文摘要
学生和职场人士每天都要经历考勤流程。传统的纸笔或在线平台考勤方法需要大量人力且耗时。为解决人工考勤流程的挑战，本研究探索在教育场景中使用人脸检测（FD）和人脸识别（FR）技术实现考勤流程自动化，并构建ClassVision课程考勤系统。我们还提出一款具备人机交互（HCI）功能且界面友好的网络版自动考勤系统，该系统利用实时图像处理识别和确认教室中学生身份，自动记录考勤。经测试，我们确定RetinaFace为最优人脸检测模型，将其与人脸识别验证结合，采用50×50像素的裁剪嵌入时，取得了最具前景的结果。
英文摘要
Students and working professionals have to go through the attendance process every day. Traditional methods of marking attendance using pen and paper or online platforms are human-intensive and time-consuming. To address the challenges in manual attendance processes, this research explores the use of face detection (FD) and face recognition (FR) technology to automate the attendance process, particularly in educational settings, and build a ClassVision course attendance system. We also propose an automated attendance system featuring a human-computer interaction (HCI) and user-friendly web interface that utilizes real-time image processing to identify and recognize students in classrooms and automatically record their attendance. We identified RetinaFace as the best face detection model, and when combined with Face Recognition for verification, it provided the most promising results with a cropped embedding of 50x50 pixels.
Journal refProc. 2024 Fourth International Conference on Digital Data Processing (DDP), 2024, pp. 27-34
DOI：10.1109/DDP64453.2024.00015
