Unsupervised Gait Recognition with Selective Fusion

TL;DR

This paper introduces an unsupervised gait recognition framework that leverages selective fusion techniques to improve clustering accuracy across different clothing and viewing conditions without requiring labeled data.

Contribution

It proposes a novel unsupervised gait recognition method with cluster-based contrastive learning and selective fusion strategies to handle appearance and view variations.

Findings

Improved rank-1 accuracy across clothing changes

Enhanced performance on front/back view sequences

Effective unsupervised gait recognition without labeled datasets

Abstract

Previous gait recognition methods primarily trained on labeled datasets, which require painful labeling effort. However, using a pre-trained model on a new dataset without fine-tuning can lead to significant performance degradation. So to make the pre-trained gait recognition model able to be fine-tuned on unlabeled datasets, we propose a new task: Unsupervised Gait Recognition (UGR). We introduce a new cluster-based baseline to solve UGR with cluster-level contrastive learning. But we further find more challenges this task meets. First, sequences of the same person in different clothes tend to cluster separately due to the significant appearance changes. Second, sequences taken from 0{\deg} and 180{\deg} views lack walking postures and do not cluster with sequences taken from other views. To address these challenges, we propose a Selective Fusion method, which includes Selective Cluster Fusion (SCF) and Selective Sample Fusion (SSF). With SCF, we merge matched clusters of the same person wearing different clothes by updating the cluster-level memory bank with a multi-cluster update strategy. And in SSF, we merge sequences taken from front/back views gradually with curriculum learning. Extensive experiments show the effectiveness of our method in improving the rank-1 accuracy in walking with different coats condition and front/back views conditions.