智能感知与计算研究中心知识库

Gait recognition is an important yet challenging problem in computer vision. The changing view of gait is one of the most challenging factors, which could greatly affect the accuracy of cross-view gait recognition. In this paper, we propose a Two-Stream Generative Adversarial Network (TS-GAN) for cross-view gait recognition. For any view of gait representations, GAN can restore it to the corresponding standard view, to learn view invariant gait features. To achieve this goal, TS-GAN has two streams : (1) the global-stream can learn global contexts, and (2) the part-stream can learn local details. We combine the two streams to learn final identities. Moreover, we add a pixel-wise loss along with the generators of GAN to restore the gait details in pixel-level. We evaluate the proposed method on two widely used gait databases: CASIA-B and OU-ISIR. Experiment results show that our approach outperforms the compared state-of-the-art approaches. (C) 2019 Elsevier B.V. All rights reserved.