Hand posture recognition via joint feature sparse representation

Chuqing Cao; Ying Sun; Ruifeng Li; Lin Chen

doi:10.1117/1.3662884

1 December 2011 Hand posture recognition via joint feature sparse representation

Chuqing Cao, Ying Sun, Ruifeng Li, Lin Chen

Author Affiliations +

Funded by: National High-tech R&D Program (863 Program) of China, National Natural Science Fund of China, State Key Laboratory of Robotics and System, Harbin Institute of Technology

Optical Engineering, Vol. 50, Issue 12, 127210 (December 2011). https://doi.org/10.1117/1.3662884

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.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Chuqing Cao, Ying Sun, Ruifeng Li, and Lin Chen "Hand posture recognition via joint feature sparse representation," Optical Engineering 50(12), 127210 (1 December 2011). https://doi.org/10.1117/1.3662884

Published: 1 December 2011

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