Gait recognition with adaptively fused GEI parts

Abstract

Though the general gait energy image (GEI) preserves static and dynamic information, most GEI-based gait recognition approaches do not fully exploit it, which leads to inferior performance under the conditions of appearance change, dynamic variation and viewpoint variation. Therefore, this paper proposes a novel Silhouette-based method called GEI parts (GEIs) to identify individuals. The GEIs divides GEI, as the gray-value of GEI indicates different motion of body part. Furthermore, this paper uses k-nearest neighbor as classifier and develops a feature fusion method by adding scores to the recognition results of each GEI part. The proposed method is tested on publicly available CASIA-B dataset under different conditions, by using: (1) different GEI parts individually; (2) adaptively fused GEI parts. The experimental results show that with our proposed adaptive GEIs fusion on the dynamic-static information of walking, the fused GEIs outperforms the state-of-the-art GEI.