Omni-Domain Feature Extraction Method for Gait Recognition

Jiwei Wan, Huimin Zhao, Rui Li (), Rongjun Chen and Tuanjie Wei
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Jiwei Wan: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Huimin Zhao: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Rui Li: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Rongjun Chen: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Tuanjie Wei: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China

Mathematics, 2023, vol. 11, issue 12, 1-19

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%.

Keywords: gait recognition; Omni-Domain Feature Extraction; temporal sensitive; dynamic motion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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