Learning discriminative representation with global and fine-grained features for cross-view gait recognition

In this study, we examine the cross-view gait recognition problem. Many existing methods establish global feature representation based on the whole human body shape. However, they ignore some important details of different parts of the human body. In the latest literature, positioning partial regions to learn fine-grained features has been verified to be effective in human identification. But they only consider coarse fine-grained features and ignore the relationship between neighboring regions. Taken the above insights together, we propose a novel model called GaitGP, which learns both important details through fine-grained features and the relationship between neighboring regions through global features. Our GaitGP model mainly consists of the following two aspects. First, we propose a Channel-Attention Feature Extractor (CAFE) to extract the global features, which aggregates the channel-level attention to enhance the spatial information in a novel convolutional component. Second, we present the Global and Partial Feature Combiner (GPFC) to learn different fine-grained features, and combine them with the global features extracted by the CAFE to obtain the relevant information between neighboring regions. Experimental results on the CASIA gait recognition dataset B (CASIA-B), The OU-ISIR gait database, multi-view large population dataset, and The OU-ISIR gait database gait datasets show that our method is superior to the state-of-the-art cross-view gait recognition methods.