Performance Evaluation of 3D Human Pose Estimation Algorithms for Skeleton-Based Gait Recognition

Gait recognition identifies individuals based on their unique walking patterns and has traditionally relied on silhouette-based methods for their higher accuracy. However, advancements in 3D Human Pose Estimation (HPE) algorithms have enabled skeleton-based methods to emerge as a promising alternative. In this work, we quantitatively evaluate the performance of state-of-the-art 3D HPE algorithms in a modular skeleton-based gait recognition pipeline. Our system combines 3D pose extraction from RGB video with a contrastive attention-based gait encoder, enabling direct comparisons of HPE models based on their impact on gait recognition accuracy. Experiments on the CASIA-B dataset demonstrate how improvements in pose estimation accuracy translate into recognition performance, providing new insights into the suitability of 3D HPE models for this task. Our findings establish a benchmark for skeleton-based methods and highlight their growing viability for real-world gait recognition applications.