关键词: 课堂考勤, 目标检测, 人脸姿态, 人脸识别, 人体检测

Abstract: 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 R-CNN 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 Neural Network) algorithm and FSA(Fine-grained Structure Aggregation)-Net algorithm in order to filter the frontal face image of every student. Finally, the FaceNet algorithm was used to extract the features of the filtered student frontal face images for training or recognition of Support Vector Machine (SVM) classifiers. Experimental results showed that, compared with the Tiny-face algorithm, when the Intersection over Union (IOU) was 0.75, the body detection algorithm had the Average Precision (AP) value increased by about 36% and the detection time reduced by 57%; compared with the method of establishing a multi-pose face database, the method of face angle filtering improved the recognition rate by 4%; and the accuracy of student recognition in the entire classroom was close to 100% in most cases. The proposed method simplifies the student registration process, improves the face recognition rate, and provides new ideas for solving the problem of face missed detection.

Key words: class attendance, object detection, face pose, face recognition, body detection