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Gait, as one of unique biometric features, has the advantage of beingrecognized from a long distance away, can be widely used in public security.Considering 3D pose estimation is more challenging than 2D pose estimation inpractice , we research on using 2D joints to recognize gait in this paper, anda new model-based gait recognition method JointsGait is put forward to extractgait information from 2D human body joints. Appearance-based gait recognitionalgorithms are prevalent before. However, appearance features suffer fromexternal factors which can cause drastic appearance variations, e.g. clothing.Unlike previous approaches, JointsGait firstly extracted spatio-temporalfeatures from 2D joints using gait graph convolutional networks, which are lessinterfered by external factors. Secondly, Joints Relationship Pyramid Mapping(JRPM) are proposed to map spatio-temporal gait features into a discriminativefeature space with biological advantages according to the relationship of humanjoints when people are walking at various scales. Finally, we design a fusionloss strategy to help the joints features to be insensitive to cross-view. Ourmethod is evaluated on two large datasets, Kinect Gait Biometry Dataset andCASIA-B. On Kinect Gait Biometry Dataset database, JointsGait only usescorresponding 2D coordinates of joints, but achieves satisfactory recognitionaccuracy compared with those model-based algorithms using 3D joints. On CASIA-Bdatabase, the proposed method greatly outperforms advanced model-based methodsin all walking conditions, even performs superior to state-of-artappearance-based methods when clothing seriously affect people's appearance.The experimental results demonstrate that JointsGait achieves the state-of-artperformance despite the low dimensional feature (2D body joints) and is lessaffected by the view variations and clothing variation.

JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping