Learning 3D spatiotemporal gait feature by convolutional network for person identification

For person identification in non-interaction biometric systems, gait recognition has been recently encouraged in literature and industrial applications instead of face recognition. Although numerous advanced methods that learn object appearance by conventional machine learning models have been discussed in the last decade, most of them are strongly sensitive to scene background motion. In this research, we address the drawbacks of existing works by comprehensively studying gait information from 3D human skeleton data with a deep learning-based identifier. To capture the statistic gait information in the spatial dimension, we first extract the geometric gait features of joint distance and orientation. The dynamic gait information is then obtained by calculating the temporal description features with the mean and standard deviation of geometric features. Accordingly, the fully gait information of an individual is finally learned via a compact Deep Convolutional Neural Network which is explicitly designed with multiple stacks of asymmetric convolutional filters to fully gain the spatial correlation of in-frame body joints and the temporal relation of frame-wise posture at multiple scales. Based on the experimental results evaluated on four benchmark 3D gait datasets commonly used for person identification, including UPCV Gait, UPCV Gait K2, KS20 VisLab Multi-View Kinect Skeleton, and SDUGait, the proposed method presents the superior performance over that of several state-of-the-art approaches while maintaining a low computational capacity.