期刊论文

Non-perception class attendance method based on student body detection

中文

计算机应用

1001-9081

2020

40

9

2519-2524

10.11772/j.issn.1001-9081.2020010067

湖北省高等学校省级教学研究项目（2018089）~~；

本校为第一机构

针对基于人脸识别的课堂考勤系统漏检和低识别率的问题,采用主、从双摄像机设备,提出一种联合学生人体检测和人脸角度筛选的方法。首先通过Mask R-CNN算法检测主摄像机拍摄图中的学生人体位置;然后控制从摄像机(PTZ相机)依次获取每位学生的高质量放大图像;再通过MTCNN算法和FSA-Net算法从中检测并识别出人脸姿态,筛选出每位学生的正脸图像;最后对筛选出的学生正脸图像使用FaceNet算法提取人脸特征,用于支持向量机(SVM)分类器的训练或识别。实验结果表明,与Tiny-face算法相比,人体检测算法在重叠比(IOU)为0.75时平均精度(AP)值提高了约36%且检测耗时减少了57%;与建立多姿态人脸数据库的方法相比,...

Concerning the missed detection and low recognition rate in the class attendance system based on face recognition,a method that combines student body detection and face angle filtering was proposed by applying the master and slave dual-camera device. First,the bodies of students were detected from the photograph of master camera by the Mask RCNN algorithm. Then,the slave camera(PTZ(Pan/Tilt/Zoom)camera)was controlled to acquire high-quality magnified image of each student in turn. Next,the face poses were detected and recognized in the magnified images through MTCNN (Multi-Task Convolutional N...