Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis

PATTERN RECOGNITION

卷:60

Article

Luo, Jian¹  Tang, Jin¹  Tjahjadi, Tardi²  Xiao, Xiaoming¹ 

[1] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China

[2] Univ Warwick, Sch Engn, Gibbet Hill Rd, Coventry CV4 7AL, W Midlands, England

关键词: Gait recognition;    Human identification;    Silhouette;    Sparse representation;   

DOI  :  10.1016/j.patcog.2016.05.030

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier Ltd. All rights reserved.

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