A GPU-paralleled implementation of an enhanced face recognition algorithm

Hao Chen; Xiyang Liu; Shuai Shao; Jiguo Zan

doi:10.1117/12.2012506

13 March 2013 A GPU-paralleled implementation of an enhanced face recognition algorithm

Hao Chen, Xiyang Liu, Shuai Shao, Jiguo Zan

Proceedings Volume 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing; 87830C (2013) https://doi.org/10.1117/12.2012506
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

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.

Citation Download Citation

Hao Chen, Xiyang Liu, Shuai Shao, and Jiguo Zan "A GPU-paralleled implementation of an enhanced face recognition algorithm", Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 87830C (13 March 2013); https://doi.org/10.1117/12.2012506

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KEYWORDS

Facial recognition systems

Detection and tracking algorithms

MATLAB

Computer programming

Compressed sensing

Parallel computing

Computer architecture

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