Face recognition method and system based on spatio-temporal feature fusion and sample attention enhancement

Abstract

The invention discloses a face recognition method and system based on spatio-temporal feature fusion and sample attention enhancement. The method comprises the following steps: obtaining a specific target face sequence in a video through face detection, and scoring the specific target face sequence; performing time feature extraction on the face sequence by using a time sequence processing algorithm ConvGRU; selecting an image with the highest score from the face sequence as a key frame; sending into a Resnet50 network to extract three feature maps with different depths, and calculating by using a spatial feature fusion algorithm ASFF to obtain spatial features; and finally, splicing the obtained time features and the space features in channel dimensions, sending the spliced features to a global average pooling layer and a full connection layer, and training the model by using a proposed ADAM-Softmax loss function. The ADAM-Softmax loss function can adaptively enhance the attention of samples with large intra-class difference, so that the model can achieve high recognition accuracy while rapid convergence is achieved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a face recognition method and system based on spatio-temporal feature fusion and sample attention enhancement. Background technique [0002] In recent years, with the rapid development of deep learning technology, face recognition technology based on static images has made great progress. This is due to the constantly updated advanced neural network architecture and the unremitting efforts of scientific researchers in the theory of feature extraction. The progress of face recognition technology based on static images has also promoted the successful implementation of related application products. Relying on the powerful feature extraction capability of CNN network and the real-time performance of lightweight neural network, face recognition has been used in campus security, life services, etc. The field has achieved relatively good results. [0003] However,...

AI Technical Summary

Problems solved by technology

However, in practical applications, especially in video surveillance scenarios, the recognized objects or targets are usually imperceptible, and the time and space that appear in the video have certain randomness.

There is no guarantee that the captured images fully meet the technical requirements for face recognition based on static images

Therefore, directly applying static face recognition technology to video surveillance scenes will inevitably lead to a reduction in recognition accuracy, which brings certain challenges to the application

Images