关键词: 步态识别, 深度卷积沙漏网络, 运动特征, 动态时间规整

Abstract: A new approach for gait recognition is proposed based on stacked depth convolution hourglass network. In order to locate the joints in human modeling accurately, the convolution hourglass network based on deep convolution network is used to extract the joint coordinates of gait image, and the angle between elbow joint and knee joint is calculated as motion characteristics. Considering the influence of the change of walking speed, the dynamic time warping algorithm is used to calculate the distance of the feature sequence. Finally, the nearest neighbor classifier is used to classify the results accurately. Experimental results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-GAID for gait recognition.

Key words: gait recognition, depth convolution hourglass network, motion characteristics, dynamic time warping