Cross-View Gait Recognition Based on Feature Fusion

Compared to face recognition, gait recognition is one of the most promising video biometric recognition technologies given that gait images can be readily captured at a distance and gait characteristics are robust to appearance camouflage. A lot of existing gait recognition methods aim at a single scene such as fixed cameras, but the recognition accuracy will decrease sharply if the viewpoints are changed. In this paper, we improve the existing methods and propose a cross-view gait recognition method based on feature fusion. Firstly, a multi-scale feature fusion module is proposed to extract the features of gait sequences with different granularities. Then, a dual-path structure is introduced to learn global appearance features and fine-grained local features, respectively. The features of two paths are gradually merged as the network deepens to obtain the complementary information. In the last feature mapping stage, the Generalized-Mean pooling is used to favour discriminative representation. Extensive experiments on the public dataset CASIA-B show that our method can achieve state-of-the-art recognition performance.