Open AccessDiscriminating features learning in hand gesture classification
Author(s) -
Jiang Feng,
Wang Cuihua,
Gao Yang,
Wu Shen,
Zhao Debin
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0426
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , feature extraction , feature (linguistics) , discriminative model , rgb color model , linear discriminant analysis , histogram , subspace topology , projection (relational algebra) , computer vision , local binary patterns , contextual image classification , gesture recognition , feature vector , gesture , image (mathematics) , philosophy , linguistics , algorithm
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.