Causality-inspired Discriminative Feature Learning in Triple Domains for Gait Recognition

• URL: http://arxiv.org/abs/2407.12519v1
• Date: Wed, 17 Jul 2024 12:16:44 GMT
• Title: Causality-inspired Discriminative Feature Learning in Triple Domains for Gait Recognition
• Authors: Haijun Xiong, Bin Feng, Xinggang Wang, Wenyu Liu,
• Abstract summary: We propose CLTD, a discriminative feature learning module designed to eliminate the influence of confounders in triple domains, ie, spatial, temporal, and spectral. Specifically, we utilize the Cross Pixel-wise Attention Generator (CPAG) to generate attention distributions for factual and counterfactual features in spatial and temporal domains. Then, we introduce the Fourier Projection Head (FPH) to project spatial features into the spectral space, which preserves essential information while reducing computational costs.
• Score: 36.55724380184354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gait recognition is a biometric technology that distinguishes individuals by their walking patterns. However, previous methods face challenges when accurately extracting identity features because they often become entangled with non-identity clues. To address this challenge, we propose CLTD, a causality-inspired discriminative feature learning module designed to effectively eliminate the influence of confounders in triple domains, \ie, spatial, temporal, and spectral. Specifically, we utilize the Cross Pixel-wise Attention Generator (CPAG) to generate attention distributions for factual and counterfactual features in spatial and temporal domains. Then, we introduce the Fourier Projection Head (FPH) to project spatial features into the spectral space, which preserves essential information while reducing computational costs. Additionally, we employ an optimization method with contrastive learning to enforce semantic consistency constraints across sequences from the same subject. Our approach has demonstrated significant performance improvements on challenging datasets, proving its effectiveness. Moreover, it can be seamlessly integrated into existing gait recognition methods.

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