Frame difference energy image for gait recognition with incomplete silhouettes

The quality of human silhouettes has a direct effect on gait recognition performance. This paper proposes a robust dynamic gait representation scheme, frame difference energy image (FDEI), to suppress the influence of silhouette incompleteness. A gait cycle is first divided into clusters. The average image of each cluster is denoised and becomes the dominant energy image (DEI). FDEI representation of a frame is constructed by adding the corresponding cluster’s DEI and the positive portion of the frame difference between the former frame and the current frame. FDEI representation can preserve the kinetic and static information of each frame, even when the silhouettes are incomplete. This proposed representation scheme is tested on the CMU Mobo gait database with synthesized occlusions and the CASIA gait database (dataset B). The frieze and wavelet features are adopted and hidden Markov model (HMM) is employed for recognition. Experimental results show the superiority of FDEI representation over binary silhouettes and some other algorithms when occlusion or body portion lost appears in the gait sequences.