Research on Importance of Texture Information in Face Recognition

Article Preview

Abstract:

This paper presents importance of skin texture information in face recognition. To this end, we perform the illumination normalization on face image in order to extract texture information unaffected by illumination variation. And then apply mask image on each illumination normalized face image to obtain the corresponding texture data, which hardly contain the shape information. Face recognition experiments are carried out by using texture data. Experimental results on Yale face database B and CMU PIE database show that the texture information has considerable ability to distinguish subjects in face recognition.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1193-1196

Citation:

Online since:

January 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Xuan Zou, Josef Kittler and Kieron Messer: Illumination Invariant Face Recognition: A Survey. In: Proc. First IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp.1-8, IEEE Press, New York (2007).

DOI: 10.1109/btas.2007.4401921

Google Scholar

[2] W. Chen, M. Er and S. Wu: Illumination Compensation and Normalization for Robust Face Recognition Using Discrete Cosine Transform in Logarithm Domain. IEEE Trans. Systems, Man, and Cybernetics-Part B Vol. 36(2), pp.458-466 (2006).

DOI: 10.1109/tsmcb.2005.857353

Google Scholar

[3] T. Chen, W. Yin, X. Zhou, D. Comaniciu and T. Huang: Total Variation Models for Variable Lighting Face Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence Vol. 28(9), pp.1519-1524 (2006).

DOI: 10.1109/tpami.2006.195

Google Scholar

[4] Y.K. Park, S.L. Park and J.K. Kim: Retinex Method Based on Adaptive Smoothing for Illumination Invariant Face Recognition. Signal Processing Vol. 88(8), pp.1929-1945 (2008).

DOI: 10.1016/j.sigpro.2008.01.028

Google Scholar

[5] Chengzhe Xu: Illumination Invariant Face Recognition Using Nonlocal Total Variation in Logarithmic Domain. Applied Mechanics and Materials Vols. 241-244, pp.1652-1658 (2013), in press.

DOI: 10.4028/www.scientific.net/amm.241-244.1652

Google Scholar

[6] A. Elmoataz, O. Lezoray and S. Bougleux: Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing. IEEE Trans. Image Processing Vol. 17(7), pp.1047-1060 (2008).

DOI: 10.1109/tip.2008.924284

Google Scholar

[7] X. Zhang, M. Burger, X. Bresson and S. Osher: Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction. Tech. Rep. CAM Report 09-03, Univ. California, Los Angeles (2009).

DOI: 10.1137/090746379

Google Scholar

[8] A.S. Georghiades and P.N. Belhumeur: From Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose. IEEE Trans. Pattern Analysis and Machine Intelligence Vol. 23(2), pp.643-660 (2001).

DOI: 10.1109/34.927464

Google Scholar

[9] T. Sim, S. Baker and M. Bsat, The CMU Pose, Illumination, and Expression (PIE) Database. In: IEEE Int'l Conf. Automatic Face and Gesture Recognition, IEEE Press, New York (2002).

DOI: 10.1109/afgr.2002.1004130

Google Scholar