A Lightweight Face Recognition Algorithm for Internet of Vehicles

Abstract

In recent years, face detection and recognition technology has been widely used in many important scenarios, especially for automatic driving scenarios and future Internet of vehicles. Practical challenges such as computing resource limitation and real-time processing of edge nodes need to be fully considered. In order to cope with the problem of severe computing resources limitation on the edge of the Internet of Vehicles, this paper optimized the lightweight network model Lightened CNN for face depth feature extraction. By the batch normalization mechanism, the vector distribution of facial features of different identities is dispersed. The relative feature center distance is increased. It gives the extracted features a better classification aggregation feature. According to the design flow of face recognition algorithm, the method of face alignment and similarity measurement is studied. It is designed to complete the face recognition scheme adapted to the Internet of Vehicles scene. After experiment and analysis, the face depth feature extraction network designed in this paper occupies less embedded system resources. At the same time, the face recognition algorithm designed by combining lightweight face feature extraction network, and face alignment and similarity measurement has higher accuracy.