title: 2.5D multi-view gait recognition based on point cloud registration.
abstract: This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM.
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China. tjin@csu.edu.cn.
authors.forename: Jin
authors.initials: J
authors.lastname: Tang
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China. delphifx@csu.edu.cn.
authors.forename: Jian
authors.initials: J
authors.lastname: Luo
authors.affiliation: School of Engineering, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. t.tjahjadi@warwick.ac.uk.
authors.forename: Tardi
authors.initials: T
authors.lastname: Tjahjadi
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China. gaoyan@csu.edu.cn.
authors.forename: Yan
authors.initials: Y
authors.lastname: Gao
mesh_terms: D003195:Computer Communication Networks; D005684:Gait / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical