Gait Recognition with Clothing and Carrying Variations Based on GEI and CAPDS Features

摘要

Gait recognition is a promising technology in biometrics. The accuracy of gait recognition can be decreased by many interference variations, such as view angle, clothing and carrying. A novel method is proposed based on the Gait Energy Image (GEI) feature and Coordinate-Angle-Position-Distance Skeleton (CAPDS) feature to eliminate the interference of clothing and carrying variations. GEI is a common feature widely used in gait recognition, but it is sensitive to the change of clothing and carrying. The CAPDS proposed in this paper is robust to the clothing and carrying variations. They are fused in backward to complement each other for recognition. Two novel networks, the Paird ResNet (PRN) and the Temporal-Spatial Paired Network (TSPN), are designed to extract the deep features of GEI and CAPDS. The experiments evaluated on the dataset CASIA-B show that the proposed method based on the backward fusion strategy of GEI and CAPDS features can achieve better performance than most methods in gait recognition with clothing and carrying variations.