Gait recognition based on Wasserstein generating adversarial image inpainting network

Aiming at the problem of small area human occlusion in gait recognition, a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion area. In order to reduce the effect of noise on feature extraction, the stacked automatic encoder with robustness was used. In order to improve the ability of gait classification, the sparse coding was used to express and classify the gait features. Experiments results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-Gaid for gait recognition.