Sparse Representation Frontal Facial Recognition Algorithm Based on Eigenface

Bo Yang Ding; Yuan Yan  Tang; Zhen Chao  Zhang; Xue Wei Wang; Chi  Fang

doi:10.4028/www.scientific.net/AMM.347-350.3629

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350Sparse Representation Frontal Facial Recognition...

Sparse Representation Frontal Facial Recognition Algorithm Based on Eigenface

Abstract:

Face recognition continues to be a hot topic in pattern recognition and computer vision recent years. Meantime, sparse representation is a new technique utilizing compressed sensing method applied in pattern recognition research recent years. This paper comparison with PCA and SP for face recognition, makes some improvement on great performance eigenface algorithm, using sparse representation with eigenface space to achieved better result. Finally, the proposed algorithm based on eigenface sparse recognition shows that of better result.

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Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

3629-3633

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.3629

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Online since:

August 2013

Authors:

Bo Yang Ding, Yuan Yan Tang, Zhen Chao Zhang, Xue Wei Wang, Chi Fang

Keywords:

Eigenface, Principal Component Analysis (PCA), Recognition, Sparse Representation

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References

[1] W. ZHAO, R. CHELLAPPA, P. J. PHILLIPS, & A. ROSENFELD, Face Recognition: A Literature Survey, ACM Computing Surveys, 35(4), 399-458, December (2003).

DOI: 10.1145/954339.954342

Google Scholar

[2] Donoho, D.L., Compressed sensing, IEEE Trans. Inform. Theory, 52(4): 1289-1306, April (2006).

DOI: 10.1109/tit.2006.871582

Google Scholar

[3] M. Turk, & A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, 3(1), 71-86, December, (2007).

Google Scholar

[4] M. Elad. Optimized projections for compressed-sensing., IEEE Trans. SP, 55(12): 5695–5702, Dec. (2007).

DOI: 10.1109/tsp.2007.900760

Google Scholar

[5] J. Wright & Y. Ma. Dense error correction via l1-minimization., Preprint, submitted to IEEE Transactions on Information Theory, 2010, 56 (7): 3540 – 3560, (2010).

DOI: 10.1109/tit.2010.2048473

Google Scholar

[6] Donoho, D.L. & Tsaig, Y. Fast solution of l1-norm minimization problems when the solution may be sparse. Department of Statistics, Stanford University, (2006).

DOI: 10.1109/tit.2008.929958

Google Scholar

[7] J. Wright, Yi Ma; Marial, J, Sapiro, G., Huang, T.S. & Shuicheng Yan, Sparse Representation For Computer Vision and Pattern Recognition, PROCEEDINGS OF IEEE, 98(6), 1031-1044, (2010).

DOI: 10.1109/jproc.2010.2044470

Google Scholar

[8] J. Wright, Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(2), 210-227, (2009).

DOI: 10.1109/tpami.2008.79

Google Scholar