Gait Recognition on Features Fusion Using Kinect
The paper proposes a gait recognition method which is about multi-features fusion using Kinect. The data of 3D skeletal coordinates is obtained by Kinect, and the multi-features are as follows. Firstly, the human skeletal structure is treated as a rod-shaped skeletal model, and it can be simple and convenient to reflect the static characteristics of structure of the human body from the overall. Secondly, the angle of the hip joint is observed during walking so that the dynamic characteristics of the gait information are reflected from the local area. Thirdly, the key gait body postures features are selected from a special gait to reflect the characteristics of walking. Then, the three gait features information is fused, which improves the overall recognition rate. After removing the noise from the bone data, in order to fully reflect the uniqueness of the individual, gait features are extracted from multiple angles, including both static and dynamic features. For finding the center of mass, the distance from the center of mass to the main joint point are calculated to measure the change in the center of mass, and the characteristics of hip joints reflecting the changes of the lower extremity joints while walking. The paper classify each feature separately by using the Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) algorithm, which is used at the decision level. The experimental results show that the proposed method achieves a better recognition rate and has a good robustness.
KeywordsGait recognition Features fusion Kinect
This study is supported by Science and Technology Project of Guangdong (2017A010101036).
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