Student Apartment Access Control System Based on MTCNN-FaceNet Algorithm

Abstract

In response to the security management issues of student apartments, a study is conducted on a student apartment access control system based on multitasking cascaded convolutional networks and FaceNet. Firstly, a face detection model is built based on an improved multi-task cascaded convolutional network, and then a face recognition model is built using FaceNet. The results showed that the detection accuracy of the multi-task cascaded convolutional network using the improved non-maximum suppression algorithm was 98.7%, which was higher than the traditional multi-task cascaded convolutional network and effectively improved the detection performance of the multi-task cascaded convolutional network. The face detection model based on the improved multi-task cascaded convolutional network had the shortest average detection time of 361[Formula: see text]s, the highest average detection accuracy of 90.3%, an accuracy of 99%, a recall rate of 98.5%, and an F1 value of 99%. While maintaining high detection efficiency, it also ensured the accuracy of detection. The average accuracy of the mask detection method based on the MobileNet V2 network was relatively high, at 98.96%. The facial recognition model based on FaceNet achieved a recognition accuracy of 99.15% for faces without masks and 92.04% for faces with masks, with the highest accuracy and recall rates of 99.3% and 99.6%. The model constructed in the study has good application effects in face detection, which helps to improve the security of the student apartment access control system.