Research on face recognition algorithm based on FaceNet and coordinate attention

The development of information technology has made the field of deep learning face recognition develop rapidly. The traditional face detection and recognition algorithm can perform well under constrained conditions, but under unconstrained conditions, its effect will be greatly discounted when low quality images and partial occlusion of faces are encountered. Based on MTCNN and FaceNet, this paper adopts two strategies to solve the above two problems respectively. On the one hand, by introducing the face image quality assessment function to solve the problem of low quality pictures, before face detection, a quality assessment of the face image is done, and only the image whose quality score reaches the threshold can be input into the model. On the other hand, the Coordinate attention mechanism is introduced to deal with the problem of partial occlusion of the face, which improves the recognition ability of the model by adaptively enhancing the weight of the unocclusion area of the face. Experimental results show that compared with existing algorithms, the accuracy of the proposed algorithm is significantly improved.