Unsupervised Domain Adaptation with Dynamic Clustering and Contrastive Refinement for Gait Recognition

TL;DR

This paper introduces GaitDCCR, a novel unsupervised gait recognition model that employs dynamic clustering and contrastive refinement to mitigate pseudo-label noise, significantly improving recognition accuracy without labeled data.

Contribution

The paper proposes a new unsupervised gait recognition framework with dynamic clustering and contrastive refinement, addressing pseudo-label noise and enhancing model robustness.

Findings

Significant performance improvement on public gait datasets.

Effective reduction of pseudo-label noise impacts.

Robustness demonstrated in real-world scenarios.

Abstract

Gait recognition is an emerging identification technology that distinguishes individuals at long distances by analyzing individual walking patterns. Traditional techniques rely heavily on large-scale labeled datasets, which incurs high costs and significant labeling challenges. Recently, researchers have explored unsupervised gait recognition with clustering-based unsupervised domain adaptation methods and achieved notable success. However, these methods directly use pseudo-label generated by clustering and neglect pseudolabel noise caused by domain differences, which affects the effect of the model training process. To mitigate these issues, we proposed a novel model called GaitDCCR, which aims to reduce the influence of noisy pseudo labels on clustering and model training. Our approach can be divided into two main stages: clustering and training stage. In the clustering stage, we propose Dynamic Cluster Parameters (DCP) and Dynamic Weight Centroids (DWC) to improve the efficiency of clustering and obtain reliable cluster centroids. In the training stage, we employ the classical teacher-student structure and propose Confidence-based Pseudo-label Refinement (CPR) and Contrastive Teacher Module (CTM) to encourage noisy samples to converge towards clusters containing their true identities. Extensive experiments on public gait datasets have demonstrated that our simple and effective method significantly enhances the performance of unsupervised gait recognition, laying the foundation for its application in the real-world. We will release the code at https://github.com/YanSun-github/GaitDCCR upon acceptance.