2022/02/22 by Likai Wang, Ruize Han, Wang, Likai +3
Engineering · Medicine · Computer Science · #Gait Recognition and Analysis #Diabetic Foot Ulcer Assessment and Management #Hand Gesture Recognition Systems
paper · pdf · doi:10.48550/arxiv.2202.10645
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.