GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition

TL;DR

GaitGraph introduces a graph convolutional network that leverages skeleton poses from RGB images for more accurate and robust gait recognition, outperforming silhouette-based methods on standard datasets.

Contribution

This paper presents GaitGraph, a novel model-based gait recognition approach combining skeleton poses with GCNs, improving feature extraction and spatio-temporal modeling.

Findings

Achieves state-of-the-art results on CASIA-B dataset.

Provides a cleaner and more effective gait feature representation.

Demonstrates robustness in complex scenes.

Abstract

Gait recognition is a promising video-based biometric for identifying individual walking patterns from a long distance. At present, most gait recognition methods use silhouette images to represent a person in each frame. However, silhouette images can lose fine-grained spatial information, and most papers do not regard how to obtain these silhouettes in complex scenes. Furthermore, silhouette images contain not only gait features but also other visual clues that can be recognized. Hence these approaches can not be considered as strict gait recognition. We leverage recent advances in human pose estimation to estimate robust skeleton poses directly from RGB images to bring back model-based gait recognition with a cleaner representation of gait. Thus, we propose GaitGraph that combines skeleton poses with Graph Convolutional Network (GCN) to obtain a modern model-based approach for gait recognition. The main advantages are a cleaner, more elegant extraction of the gait features and the ability to incorporate powerful spatio-temporal modeling using GCN. Experiments on the popular CASIA-B gait dataset show that our method archives state-of-the-art performance in model-based gait recognition. The code and models are publicly available.