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http://dx.doi.org/10.5573/ieie.2015.52.10.129

A Robust Hybrid Method for Face Recognition Under Illumination Variation  

Choi, Sang-Il (Department of Computer Science and Engineering, Dankook University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.52, no.10, 2015 , pp. 129-136 More about this Journal
Abstract
We propose a hybrid face recognition to deal with illumination variation. For this, we extract discriminant features by using the different illumination invariant feature extraction methods. In order to utilize both advantages of each method, we evaluate the discriminant power of each feature by using the discriminant distance and then construct a composite feature with only the features that contain a large amount of discriminative information. The experimental results for the Multi-PIE, Yale B, AR and yale databases show that the proposed method outperforms an individual illumination invariant feature extraction method for all the databases.
Keywords
Face Recognition; Illumination Invariant Feature; Discriminant Feature; Hybrid Method; Composite Feature; Discriminant Distance;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 M.-Y. Cho, Y.-S. Jeong, and B.-T. Chun. "A study on face recognition performance comparison of real images with images from LED monitor," Journal of the Institute of Electronics and Information Engineers, Vol. 50, no. 5, pp. 144-149, 2013.   DOI
2 W. Zhao, et al., "Face recognition: a literature survey." ACM computing surveys, Vol. 35, no. 4, pp. 399-458, 2003.   DOI   ScienceOn
3 M. Turk and A. Pentland, "Eigenfaces for recognition," J. Cognitive Neuroscience, Vol. 3, no. 1, pp. 71-86, 1991.   DOI   ScienceOn
4 P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, "Eigenfaces vs. fisherfaces: recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, no. 7, pp. 711-720, 1997.   DOI   ScienceOn
5 H. Cevikalp, M. Neamtu, M. Wilkes, et al., "Discriminative common vectors for face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, no. 1, pp. 4-13, 2005.   DOI   ScienceOn
6 S.-I. Choi, C.-H. Choi, and N. Kwak. "Face recognition based on 2D images under illumination and pose variations," Pattern Recognition Letters, Vol. 32, no. 4, pp. 561-571, 2011.   DOI   ScienceOn
7 S.-I. Choi, C. Kim and C.-H. Choi, "Shadow compensation in 2D images for face recognition," Pattern Recognition, Vol. 40, no. 7, pp. 2118-2125, 2007.   DOI   ScienceOn
8 D. Kim, M. Sohn and S. Lee, "A study on face recognition method based on binary pattern image under varying lighting condition," Journal of the Institute of Electronics and Information Engineers, Vol. 49, no. 2, pp. 61-74, 2012.
9 D.-J. Kim, M.-K. Sohn, and S.-H. Lee, "A study on face recognition method based on binary pattern image under varying lighting condition," Journal of The Institute of Electronics Engineers, Vol. 49, no. 2, pp. 61-74, 2012.
10 S.-I. Choi, and G.-M. Jeong. "Shadow compensation using fourier analysis with application to face recognition," Signal Processing Letters, Vol. 18, no. 1, pp. 23-26, 2011.   DOI   ScienceOn
11 R. Ramamoorthi, "Analytic PCA construction for theoretical analysis of lighting variability in images of a Lambertian object," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, no. 10, pp. 1322-1333, 2002.   DOI   ScienceOn
12 X. Xie and K.-M. Lam, "An efficient illumination normalization method for face recognition," Pattern Recognition Letters, Vol. 27, no. 6. pp. 609-617, 2006.   DOI   ScienceOn
13 A.S. Georghiades and P.N. Belhumeur, "From few to many: illumination cone models for face recognition under variable lighting and pose," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, no. 2, pp. 643-660, 2001.   DOI   ScienceOn
14 S.-I. Choi, and C.-H. Choi. "An effective face recognition under illumination and pose variations," in Proc, of International Joint Conference on Neural Networks, pp. 914-919, 2007.
15 L.S. Shen, D.H. Liu, and K.M. Lam, "Illumination invariant face recognition," Pattern Recognition, Vol. 38, no. 10. pp. 1705-1716, 2005.   DOI   ScienceOn
16 T. Ahonen, A. Hadid, and M. Pietikainen. "Face description with local binary patterns: application to face recognition," Pattern Analysis and Machine Intelligence, IEEE Transactions on., Vol. 28, no. 12, pp. 2037-2041, 2006.   DOI   ScienceOn
17 B. Froba, and A. Ernst. "Face detection with the modified census transform," Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings, pp. 91-96, 2004.
18 R.C. Gonzalez, and R.E. Woods, Digital Image Processing, Prentice Hall, 2002, 2Ed.
19 Y. Adini, Y. Moses, and S. Ullman. "Face recognition: The problem of compensating for changes in illumination direction," Pattern Analysis and Machine Intelligence, Vol. 19, no. 7, pp. 721-732, 1997.   DOI   ScienceOn
20 J. Liang, S. Yang, and A. Winstanley. "Invariant optimal feature selection: a distance discriminant and feature ranking based solution," Pattern Recognition, Vol. 41, no. 5, pp. 1429-1439, 2008.   DOI   ScienceOn
21 T. Sim, S. Baker, M. Bsat. "The CMU pose illumination and expression (PIE) database," in: IEEE International Conference on Automatic Face and Gesture Recognition, May 2002.
22 R. Gross, et al. "Multi-pie." Image and Vision Computing, Vol. 28, no. 5, pp. 807-813, 2010.   DOI   ScienceOn
23 A.M. Martinez and R. Benavente, The AR Face Database. CVC Technical Report #24, June 1998.
24 Center for Computational Vision and Control, Yale University, The Yale Face Database, http://cvc.yale.edu/projects/yalefaces/yalefaces.html.