This paper proposes an arbitrary view gait recognition method where the gait recognition is performed in 3-dimensional (3D) to be robust to variation in speed, inclined plane and clothing, and in the presence of a carried item. 3D parametric gait models in a gait period are reconstructed by an optimized 3D human pose, shape and simulated clothes estimation method using multiview gait silhouettes. The gait estimation involves morphing a new subject with constant semantic constraints using silhouette cost function as observations. Using a clothes-independent 3D parametric gait model reconstruction method, gait models of different subjects with various postures in a cycle are obtained and used as galleries to construct 3D gait dictionary. Using a carrying-items posture synthesized model, virtual gait models with different carrying-items postures are synthesized to further construct an over-complete 3D gait dictionary. A self-occlusion optimized simultaneous sparse representation model is also introduced to achieve high robustness in limited gait frames. Experimental analyses on CASIA B dataset and CMU MoBo dataset show a significant performance gain in terms of accuracy and robustness.
展开▼
