Progressive Spatio-Temporal Feature Extraction Model For Gait Recognition

As a new biometric feature, gait brings new possibilities for personal recognition. At present, gait recognition methods mainly extract appearance features, but seldom use temporal information, such as the method based on gait energy images (GEI) to fuse gait sequence into one image. In order to mine the motion patterns contained in gait sequences, this paper proposes a model that can gradually fuse temporal features while extracting spatial features to achieve the spatiotemporal feature extraction: 1) the model mines temporal information by passing partial channels of feature maps and fusing features of adjacent frames; 2) the model adapts the partbaesd method to split feature map into several parts, which can refine the spatial features. Extensive experiments on the challenging datasets CASIA-B demonstrate the superiority and effectiveness of our proposed model.