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Optical Engineering

Hand posture recognition via joint feature sparse representation
Author(s): Chuqing Cao; Ying Sun; Ruifeng Li; Lin Chen
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Paper Abstract

In this study, we cast hand posture recognition as a sparse representation problem, and propose a novel approach called joint feature sparse representation classifier for efficient and accurate sparse representation based on multiple features. By integrating different features for sparse representation, including gray-level, texture, and shape feature, the proposed method can fuse benefits of each feature and hence is robust to partial occlusion and varying illumination. Additionally, a new database optimization method is introduced to improve computational speed. Experimental results, based on public and self-build databases, show that our method performs well compared to the state-of-the-art methods for hand posture recognition.

Paper Details

Date Published: 1 December 2011
PDF: 11 pages
Opt. Eng. 50(12) 127210 doi: 10.1117/1.3662884
Published in: Optical Engineering Volume 50, Issue 12
Show Author Affiliations
Chuqing Cao, Harbin Institute of Technology (China)
Ying Sun, National Univ. of Singapore (Singapore)
Ruifeng Li, Harbin Institute of Technology (China)
Lin Chen, Harbin Institute of Technology (China)

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