Discriminating features learning in hand gesture classification. Issue 5 (1st October 2015)

• Record Type: Journal Article
• Authors: Jiang, Feng; Wang, Cuihua; Gao, Yang; Wu, Shen; Zhao, Debin
• Is Part Of: IET computer vision. Volume 9:Issue 5(2015)
• Page Start: 673
• Page End: 680
• Publication Date: 2015-10-01
• Subjects: gesture recognition -- learning (artificial intelligence) -- feature extraction -- image colour analysis -- image classification; hand gesture classification -- features learning discrimination -- Kinect -- HGR research -- features extraction -- red, green and blue images -- depth images -- histogram of oriented gradient feature -- local binary pattern feature -- structure feature -- three-dimensional voxel feature -- deflation orthogonal discriminant analysis -- supervised subspace projection -- RGB images -- hand gesture recognition; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2014.0426
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : The advent and popularity of Kinect provides a new choice and opportunity for hand gesture recognition (HGR) research. In this study, the authors propose a discriminating features extraction for HGR, in which features from red, green and blue (RGB) images and depth images are both explored. More specifically, histogram of oriented gradient feature, local binary pattern feature, structure feature and three‐dimensional voxel feature are first extracted from RGB images and depth images, then these features are further reduced with a novel deflation orthogonal discriminant analysis, which enhances the discriminative ability of the features with supervised subspace projection. The extensive experimental results show that the proposed method improves the HGR performance significantly.