Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction

Abstract

As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial–temporal graph convolutional networks’ bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust.

Multi-View Gait Analysis by Temporal Geometric Features of Human Body Parts

“…Their proposed method outperformed the other state-of-the-art methods by 15% of rank-1 accuracy. K. Han et al proposed a discontinuous gait image recognition based on the extracted keypoints of the human skeleton [23]. They aimed to overcome the situation of discontinuity in the gait images.…”

GaitSG: Gait Recognition with SMPLs in Graph Structure

“…Jun et al [26] leverage a composition of the graph convolutional network, the recurrent neural network, and the artificial neural network to encode skeleton sequences, joint angle sequences, and gait parameters. Han et al [27] propose a discontinuous frame screening module for the front end of the feature extraction part, to filter rich information. However, it is challenging to capture global appearance descriptions of a human using skeleton-based approaches.…”