From FastPoseGait to GPGait++: Bridging the Past and Future for Pose-Based Gait Recognition

Shibei Meng, Yang Fu, Saihui Hou, Xuecai Hu, Chunshui Cao, Xu Liu, Yongzhen Huang

Abstract: Recent studies on pose-based gait recognition have underscored the potential of utilizing such fundamental data to achieve superior outcomes. Nonetheless, the development of current pose-based methods faces significant obstacles due to several critical issues: (1) Misaligned Settings, which results in a lack of thorough and unbiased comparative analysis. (2) Inferior Performance, which causes diminished focus on pose-based gait representations. (3) Limited Generalization, which hinders the effective application in real-world scenarios. Focused on tackling the aforementioned challenges, our study introduces a comprehensive benchmark and a versatile approach to bridge the past and future for pose-based gait recognition. First, we revisit previous pose-based methods and make great efforts to establish a unified framework, FastPoseGait, aiming at a fair and comprehensive comparison investigation with consistent experimental settings and a more stable training process. Then, within this framework, we propose GPGait++, a generalized pose-based gait recognition method featuring a human-oriented input and part-aware modeling, intended to enhance the generalization ability and discriminative power across diverse environments and camera viewpoints. Experiments on six public gait recognition datasets reveal that our unified framework significantly enhances the performance of previous approaches, and GPGait++ exhibits state-of-the-art cross-domain capabilities compared to existing pose-based methods, marking a significant advancement in the field of pose-based gait recognition.