Fast Face Recognition on GPUs

Yi Lei; Xiao Ya Fan; Meng Zhang

doi:10.4028/www.scientific.net/AMR.905.543

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 905Fast Face Recognition on GPUs

Fast Face Recognition on GPUs

Abstract:

Face recognition is popular in the field of pattern recognition and image processing. However, traditional recognition technologies spend too long there are a lot of images to be recognized or trained for great accuracy in the recognition. Parallel computing is an effective way to improve the processing speed. With the improvement of GPU performance, its widely applied in computing-concentrated data operations. This paper presents a study of performance speedup achieved by applying GPU for face recognition based on PCA (Principal Component Analysis) algorithm. We successfully accelerated the testing phase by 6868-folds compared to a sequential C implementation when it has 100 test images and 2400 training images.

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Periodical:

Advanced Materials Research (Volume 905)

Pages:

543-547

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.905.543

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Online since:

April 2014

Authors:

Yi Lei, Xiao Ya Fan, Meng Zhang

Keywords:

CUDA, Face Recognition, GPU, Parallel Computing, PCA

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References

[1] Parallel Programming and Computing Platform, http: /www. nvidia. com/object/cuda_home_new. html, (2013).

Google Scholar

[2] C. M. Wittenbrink, E. Kilgariff, A. Prabhu, Fermi GF100 GPU Architecture, IEEE Micro, vol. 31, no. 2, Mar. 2011, pp.50-59.

DOI: 10.1109/mm.2011.24

Google Scholar

[3] L.I. Smith, A Tutorial on Principal Components Analysis, http: /www. cs. otago. ac. nz/cosc453/student_tutorials/principal_components. pdf, (2009).

Google Scholar

[4] OpenCV Library, http: /opencv. org. (2013).

Google Scholar

[5] H. Bae and S. Kim, Real-time face detection and recognition using hybrid information extracted from face space and facial features, Image and Vision Computing, vol. 23, Nov. 2005, pp.1181-1191.

DOI: 10.1016/j.imavis.2005.07.017

Google Scholar

[6] M. Turk and A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, vol. 3, 1991, pp.71-86.

Google Scholar

[7] I. T. Jolliffe, Principal Component Analysis, Springer –Verlag: New York, (1986).

Google Scholar

[8] Numaan. A and Sibi. A, CUDA Accelerated Face Recognition, July (2010).

Google Scholar

[9] The Database of Faces, http: /www. cl. cam. ac. uk/research/dtg/attarchive/facedatabase. html. (2013).

Google Scholar

[10] R Hewitt, Face Recognition with Eigenface, SERVO Magazine, April (2007).

Google Scholar