A Gait Recognition Method Based on the Combination of Human Body Posture and Human Body Contour

Abstract

Aiming at the issues that affect the gait recognition, such as outfit changes and carry-on-objects during gait recognition, this paper proposes a gait recognition method based on the improved GaitSet network, which relies on the fusion of human posture and human contour. This method introduces key points of human posture, and improves the precision of human contour extraction by introducing the key points of human posture. At the same time, the human posture map composed of the key points of human posture is used as the synchronous attribute of the contour map to extract gait features. Because key points focus on the inherent walking characteristics of human body, they are not affected by the external information such as clothing and carrying objects. Combined with the rich gait change attributes of human contour, it can effectively improve the accuracy and robustness of the gait recognition model. In scenes of human body wearing coats, our experimental results show that the accuracy of the proposed method in CASIA-B gait data set and self-built database can reach over 73.5% accuracy.