Cross-View Gait Recognition via View Information Elimination Mechanism - Shuai Zhang; Chibiao Liu (IEEE Access, 2024)
Gait recognition is a new and promising biometric identification method, which has many advantages, such as requiring no contact, long-distance, and hard to imitate. However, gait recognition faces many challenges in real-life scenarios, including occlusion, diverse view angles, cloth change and carrying variances. Among those, the change of viewpoints would be one of the trickiest factors. Thus, effectively mitigating or entirely eliminating the influence of perspective factors stands as a viable approach to enhance the performance of gait recognition. A novel mechanism for perspective elimination in gait recognition is proposed in this paper, aimed at mitigating the impact of viewpoints. Firstly, an adversarial learning framework is devised between the feature extractor and the perspective classifier, which confounds the discriminate ability of the perspective classifier regarding gait features, thereby reducing the influence of perspective from the feature-wise. Secondly, a redesigned center loss is proposed to draw the features with the same ID from different views close to identity centers while increasing the inter-class distances. Finally, the view-invariant identity-wise gait features are learned. The proposed method achieves mean Rank-1 = 99.2%, 97.6%, 94.0% on CASIA-B (NM, BG, CL), and mean Rank-1 = 91.0% on OU-MVLP, demonstrate the effectiveness of our method.