Gaussian Filtered Gait Energy Template and Centroid Corner Distance Features for Human Gait Recognition

Abstract

One of the convincing and latest biometric systems is gait recognition because of its ability to unobtrusively identify an individual at a distance and with low-resolution images. This study proposes an efficient method to enhance the performance of the gait detection system. The gait silhouette images are initially processed with two gait portrayal methods as the feature resources: Gait Energy Image (GEI) and Gaussian Filtered-Gait Energy Image (GF-GEI). Further, an effort has been made to present a statistical shape examination method, which is established on GF-GEI, and it is divided into six independent horizontal segments. The centroid corner distance features obtained from these horizontal segments forms the feature vector of the image. The proposed method is assessed on the widely used CASIA A, CASIA B, and OU-ISIR D gait datasets. The empirical results illustrate that the performance of the proposed approach is promising and surpasses some state-of-the-art gait identification methods recorded in literature. © 2019 IEEE.