LAUSR

At present, face recognition algorithms are facing some problems with poor face tracking and low real-time performance in multi-target recognition scenarios. This paper details a multi-target face real-time detection tracking and recognition algorithm, including three methods of fast-tracking, fast detection, and quick recognition. The first step offers a new network based on GOTURN for achieving fast face tracking. The prior information of the previous frame image used to predict the position of the face boxes at the current frame. The second step is based on MTCNN for face detection, using the prior information of the present structure to avoid generating massive of invalid candidate boxes, thereby achieving rapid detection of faces. Finally, fast face recognition realized by reduced MobileFaceNet. By avoiding repeated exposure and repeated identification of the same target, the algorithm successfully transforms a multi-target scene into a single-target scene. On the OTB2015 and 300_VW test sets, the evaluation trackers tracked faces with an accuracy rate of 92.2% and 99.6% respectively. On the Xiph test set, multi-target detection and tracking face speed reached 102fps on the CPU. Compared with the original MobileFaceNet, the streamlined network has an accuracy rate of 99.1% on LFW, the feature extraction speed increased by 25%, and the model size reduced by 45%. Experimental results show that the algorithm has high recognition accuracy and real-time performance in multi-target recognition scenes.