MTCNN and FACENET Based Access Control System for Face Detection and Recognition

Face detection and recognition is one of the research hotspots in the field of computer vision, which is widely used in video surveillance and identity matching. The traditional algorithms of face detection include AdaBoost, Haar-like, DPM, etc. These algorithms use face geometric features and template matching for face detection, which is difficult to consider both detection speed and accuracy. In order to solve this problem, this paper proposes an improved FaceNet network. The multi-task cascaded convolutional neural networks (MTCNN) is used to achieve rapid face detection and face alignment, and then the FaceNet with improved loss function is used to realize face verification and recognition with high accuracy. We compare the face detection and recognition performance of our network framework with the traditional algorithms and some deep learning algorithms. The experimental results verify that the improved FaceNet can meet the requirements of real-time recognition, and the recognition accuracy reaches 99.85%, which meets the actual demand. It can be effectively applied to face detection and recognition in the Access Control System.