Exploring Generalized Gait Recognition: Reducing Redundancy and Noise within Indoor and Outdoor Datasets

Generalized gait recognition, which aims to achieve robust performance across diverse domains, remains a challenging problem due to severe domain shifts in viewpoints, appearances, and environments. While mixed-dataset training is widely used to enhance generalization, it introduces new obstacles including inter-dataset optimization conflicts and redundant or noisy samples, both of which hinder effective representation learning. To address these challenges, we propose a unified framework that systematically improves cross-domain gait recognition. First, we design a disentangled triplet loss that isolates supervision signals across datasets, mitigating gradient conflicts during optimization. Second, we introduce a targeted dataset distillation strategy that filters out the least informative 20\% of training samples based on feature redundancy and prediction uncertainty, enhancing data efficiency. Extensive experiments on CASIA-B, OU-MVLP, Gait3D, and GREW demonstrate that our method significantly improves cross-dataset recognition for both GaitBase and DeepGaitV2 backbones, without sacrificing source-domain accuracy. Code will be released atthis https URL.

@article{zhou2025_2505.15176,
  title={ Exploring Generalized Gait Recognition: Reducing Redundancy and Noise within Indoor and Outdoor Datasets },
  author={ Qian Zhou and Xianda Guo and Jilong Wang and Chuanfu Shen and Zhongyuan Wang and Hua Zou and Qin Zou and Chao Liang and Chen Long and Gang Wu },
  journal={arXiv preprint arXiv:2505.15176},
  year={ 2025 }
}