Combining the Silhouette and Skeleton Data for Gait Recognition

• URL: http://arxiv.org/abs/2202.10645v3
• Date: Fri, 24 Mar 2023 07:14:27 GMT
• Title: Combining the Silhouette and Skeleton Data for Gait Recognition
• Authors: Likai Wang, Ruize Han, Wei Feng
• Abstract summary: Two dominant gait recognition works are appearance-based and model-based, which extract features from silhouettes and skeletons, respectively. This paper proposes a CNN-based branch taking silhouettes as input and a GCN-based branch taking skeletons as input. For better gait representation in the GCN-based branch, we present a fully connected graph convolution operator to integrate multi-scale graph convolutions.
• Score: 13.345465199699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gait recognition, a long-distance biometric technology, has aroused intense interest recently. Currently, the two dominant gait recognition works are appearance-based and model-based, which extract features from silhouettes and skeletons, respectively. However, appearance-based methods are greatly affected by clothes-changing and carrying conditions, while model-based methods are limited by the accuracy of pose estimation. To tackle this challenge, a simple yet effective two-branch network is proposed in this paper, which contains a CNN-based branch taking silhouettes as input and a GCN-based branch taking skeletons as input. In addition, for better gait representation in the GCN-based branch, we present a fully connected graph convolution operator to integrate multi-scale graph convolutions and alleviate the dependence on natural joint connections. Also, we deploy a multi-dimension attention module named STC-Att to learn spatial, temporal and channel-wise attention simultaneously. The experimental results on CASIA-B and OUMVLP show that our method achieves state-of-the-art performance in various conditions.

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