GaitLRDF: gait recognition via local relevant feature representation and discriminative feature learning

Abstract

As an emerging biometric recognition technology, gait recognition has the advantages of non-contact long distance and difficult to imitate. Existing gait recognition methods perform gait recognition by using features extracted from the overall appearance or local regions of humans. However, the detailed features extracted by current gait recognition methods based on human local region lose the overall relevance of the image and the edge information of human local region. Secondly, the method based on the local area of the human body does not focus on the local parts of the human body that are less affected by clothing occlusion. To solve the above problems, this paper proposes a new gait recognition network framework GaitLRDF, which improves the accuracy and robustness of gait recognition by Local Relation Convolutional layers (LRConv) and Human Body Focusing module(HBF). LRConv can simultaneously use the global and local information of the human body, and the local detail features extracted in the module can retain the edge information of the human body. HBF can focuse on the gait parts that are less affected by clothing occlusion, and obtain more discriminative gait detail features. The experimental results show that in the three gait environments of NM, BG and CL set by CASIA-B dataset, GaitLRDF is 0.40%, 0.10% and 1.10% higher than the current most advanced method respectively. The recognition accuracy on OU-MVLP dataset reaches 91.40%.