Improving Gait Recognition with 3D Pose Estimation

Gait is a kind of attractive biometric feature for human identification in recent decades. The view, clothing, carrying and other variations are always the challenges for gait recognition. One of the possible solutions is the model based methods. In this paper, 3D pose is estimated from 2D images are used as the feature for gait recognition. So gait can be described by the motion of human body joints. Besides, the 3D pose has better capacity for view variation than the 2D pose. Experimental results also prove that in the paper. To improve the recognition rates, LSTM and CNNs are employed to extract temporal and spatial features. Compared with other model-based methods, the proposed one has achieved much better performance and is comparable with appearance-based ones. The experimental results show the proposed 3D pose based method has unique advantages in large view variation. It will have great potential with the development of pose estimation in future.