Omni-Domain Feature Extraction Method for Gait Recognition

Abstract

As a biological feature with strong spatio-temporal correlation, the current difficulty of gait recognition lies in the interference of covariates (viewpoint, clothing, etc.) in feature extraction. In order to weaken the influence of extrinsic variable changes, we propose an interval frame sampling method to capture more information about joint dynamic changes, and an Omni-Domain Feature Extraction Network. The Omni-Domain Feature Extraction Network consists of three main modules: (1) Temporal-Sensitive Feature Extractor: injects key gait temporal information into shallow spatial features to improve spatio-temporal correlation. (2) Dynamic Motion Capture: extracts temporal features of different motion and assign weights adaptively. (3) Omni-Domain Feature Balance Module: balances fine-grained spatio-temporal features, highlight decisive spatio-temporal features. Extensive experiments were conducted on two commonly used public gait datasets, showing that our method has good performance and generalization ability. In CASIA-B, we achieved an average rank-1 accuracy of 94.2% under three walking conditions. In OU-MVLP, we achieved a rank-1 accuracy of 90.5%.