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