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Proceedings Paper

A GPU-paralleled implementation of an enhanced face recognition algorithm
Author(s): Hao Chen; Xiyang Liu; Shuai Shao; Jiguo Zan
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Paper Abstract

Face recognition algorithm based on compressed sensing and sparse representation is hotly argued in these years. The scheme of this algorithm increases recognition rate as well as anti-noise capability. However, the computational cost is expensive and has become a main restricting factor for real world applications. In this paper, we introduce a GPU-accelerated hybrid variant of face recognition algorithm named parallel face recognition algorithm (pFRA). We describe here how to carry out parallel optimization design to take full advantage of many-core structure of a GPU. The pFRA is tested and compared with several other implementations under different data sample size. Finally, Our pFRA, implemented with NVIDIA GPU and Computer Unified Device Architecture (CUDA) programming model, achieves a significant speedup over the traditional CPU implementations.

Paper Details

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 87830C (13 March 2013); doi: 10.1117/12.2012506
Show Author Affiliations
Hao Chen, TCL Corporate Research Xi'an R&D Ctr. (China)
Xiyang Liu, Xidian Univ. (China)
Shuai Shao, Xidian Univ. (China)
Jiguo Zan, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 8783:
Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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