A new method for gait recognition with 2D pose estimation

Abstract

This paper proposes a new model-based gait recognition method. Different from other methods using 3D (3-dimensional) keypoint information and skeleton information, we directly stack the 2D (2-dimensional) keypoint heatmaps in the gait sequence in the time dimension, and input it into the network structure based on 3D-CNN (3-dimensional-convolutional neural network). Then, through the gait analysis on the two dimensions of time and space, the effective gait features are finally obtained. Compared with other model-based methods, this method is more clear, concise and elegant in the process of feature extraction. The test of CASIA-B dataset shows that in the model-based gait recognition method, we have competitive performance.