Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis. (December 2016)

• Record Type: Journal Article
• Authors: Luo, Jian; Tang, Jin; Tjahjadi, Tardi; Xiao, Xiaoming
• Is Part Of: Pattern recognition. Volume 60(2016:Dec.)
• Page Start: 361
• Page End: 377
• Publication Date: 2016-12
• Subjects: Gait recognition -- Human identification -- Silhouette -- Sparse representation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.05.030
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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 multi-view 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. Highlights: Introduces a clothes-independent 3D human pose and shape estimation method. Introduces a carried-item posture synthesized model for 3D gait recognition. Discusses a self-occlusion optimized gait simultaneous sparse representation model.