Beyond Sparse Keypoints: Dense Pose Modeling for Robust Gait Recognition

Gait recognition has emerged as a promising biometric technology due to its ability to operate at a distance without subject cooperation. While pose-based methods offer advantages over appearance-based approaches in robustness and interpretability, their performance has been limited by the sparse keypoint representations of current pose estimation frameworks. We identify two critical limitations: (1) incomplete motion representation due to insufficient keypoints for dynamic body parts, and (2) lack of shape information from minimal skeleton points. This paper presents DPGait, a novel framework that addresses these challenges through innovations in both upstream processing and downstream modeling. First, we enhance pose estimation by extending the standard COCO keypoint format with additional motion-sensitive points and shape-descriptive keypoints inspired by human mesh estimation. Second, we propose a divide-and-conquer modeling strategy that processes dense keypoints through group convolution with cross-group attention, coupled with multi-granularity supervision for improved training. Our comprehensive experiments demonstrate state-of-the-art performance in pose-based gait recognition, achieving 85.8% rank-1 accuracy on SUSTech1K-surpassing leading silhouette-based methods for the first time. The results validate that dense pose representation combined with our novel modeling approach significantly advances the field of gait recognition.