Gait Recognition Based on Gait Optical Flow Network with Inherent Feature Pyramid

Abstract

Gait is a kind of biological behavioral characteristic which can be recognized from a distance and has gained an increased interest nowadays. Many existing silhouette-based methods ignore the instantaneous motion of gait, which is an important factor in distinguishing people with similar shapes. To further emphasize the instantaneous motion factor in human gait, the Gait Optical Flow Image (GOFI) is proposed to add the instantaneous motion direction and intensity to original gait silhouettes. The GOFI also helps to leverage both the temporal and spatial condition noises. Then, the gait features are extracted by the Gait Optical Flow Network (GOFN), which contains a Set Transition (ST) architecture to aggregate the image-level features to the set-level features and an Inherent Feature Pyramid (IFP) to exploit the multi-scaled partial features. The combined loss function is used to evaluate the similarity between different gaits. Experiments are conducted on two widely used gait datasets, the CASIA-B and the CASIA-C. The experiments show that the GOFN performs better on both datasets, which shows the effectiveness of the GOFN.