GaitSS: Gait Slices as a Set for Gait Recognition in Interfered Sequences

Abstract

Video-based gait recognition suffers from environmental variations and deliberate disguises, which introduce information loss, redundancy, and complex temporal dependencies, ultimately degrading recognition performance. We propose GaitSS, a novel method that addresses these challenges by treating gait slices as a set rather than by maintaining a fixed spatial order within each frame. This treatment effectively mitigates the effects of misalignment, information loss, and redundancy. In addition, we introduce a temporal frame exchange augmentation (TFEA) strategy to enhance the model’s ability to capture robust temporal gait features. We introduce the GOSI dataset, specifically collected to include diverse interference conditions, such as varying lighting, camera heights, and intentional disguises. We also evaluate our method on three widely used public gait datasets: CASIA-B, OU-MVLP, and GREW, providing a comprehensive assessment. Our method outperforms existing methods, yielding rank-1 accuracies (averaged across conditions) of 94.3%, 92.4%, 80.3%, and 42.3% on the CASIA-B, OU-MVLP, GREW, and GOSI gait datasets, respectively. (42.3% is the average of NM, WH, and OC). Class activation maps show that GaitSS focuses more on discriminative features and less on interference regions, validating its superior handling of environmental variations. Ablation and validation experiments show that performance improvement is primarily due to treating gait slices as a set, rather than from image augmentation. GaitSS can be seamlessly integrated into existing gait recognition frameworks without introducing extra parameters or computational complexity, demonstrating strong potential for real-world applications.