1 December 2011 Hand posture recognition via joint feature sparse representation
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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)
Chuqing Cao, Chuqing Cao, Ying Sun, Ying Sun, Ruifeng Li, Ruifeng Li, Lin Chen, 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 . Submission:


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