Joint Metric Learning and Hierarchical Network for Gait Recognition

Gait recognition aims to identify person by walking pattern of individuals, which has a valuable application prospect in the fields of identity identification, public security and medical diagnosis. At present, most of the gait recognition methods are designed based on the human gait information. However, this paper focuses on the feature space of gait recognition feature learning, and optimizes the feature space by introducing some general criteria to improve the discrimination of depth features. In recent literature, optimizing the feature space has been proven to be beneficial for personal identification. Taken above insights together, we propose a novel gait recognition method based on joint metric learning and hierarchical network in an end-to-end manner, for improving the ability of feature identification. The contribution of this paper is two-fold. First, a joint metric learning and hierarchical network is used to realize the learning of fine-grained information and accelerated the discriminative feature learning. Second, to learn discriminative feature, we use a fuse loss function, the triplet loss and classifier loss. It guides the training process, pulls the samples in the same set close to each other, pushes the samples in different sets away from each other, and takes the silhouettes in each set as a whole. We evaluate the proposed method on two widely-used gait datasets, ie. CASIA-B and OU-LP-Bag. Experimental results demonstrate the effectiveness of the proposed method, significantly improve gait recognition performances under cross-view and bag-carrying walking conditions, respectively. Particularly, we achieves Rank-1 accuracy of 93.2% on OU-LP-Bag dataset, better than existing state-of-the art.