© The Institution of Engineering and Technology
A robust face recognition method that utilises a set of combined features is proposed to effectively conduct face recognition with images taken under diverse illumination variations. This method extracts discriminant features from different methods, both of which have different characteristics. To exploit the respective advantage of each method, the respective discriminability of the features extracted by each method is measured based on the discriminant distance criterion of each method. The experimental results show that the proposed features result in improved recognition performance under illumination variation.
References
-
-
1)
-
8. Choi, S.-I., Jeong, G.-M.: ‘Shadow compensation using Fourier analysis with application to face recognition’, IEEE Signal Process. Lett., 2011, 18, (1), pp. 23–26 (doi: 10.1109/LSP.2010.2085434).
-
2)
-
15. Martnez, A.M., Benavente, R.: ‘The AR face database’. , 1998, vol. 24.
-
3)
-
7. Lian, Z., Er, M.J.: ‘Illumination normalization for face recognition in transformed domain’, Electron. Lett., 2010, 46, (15), pp. 1060–1061 (doi: 10.1049/el.2010.1495).
-
4)
-
28. Belhumeur, P., Hespanha, J., Kriegman, D.: ‘Eigenfaces vs. Fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, pp. 711–720 (doi: 10.1109/34.598228).
-
5)
-
110. Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: ‘Multi-PIE’, Image Vis. Comput., 2010, 28, (5), pp. 807–813 (doi: 10.1016/j.imavis.2009.08.002).
-
6)
-
11. Froba, B., Ernst, A.: ‘Face detection with the modified census transform’. IEEE Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, May 2004, pp. 91–96.
-
7)
-
9. Xie, X., Lam, K.-M.: ‘An efficient illumination normalization method for face recognition’, Pattern Recognit. Lett., 2006, 27, (6), pp. 609–617 (doi: 10.1016/j.patrec.2005.09.026).
-
8)
-
28. Ahonen, T., Hadid, A., Pietikainen, M.: ‘Face description with local binary patterns: application to face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (12), pp. 2037–2041 (doi: 10.1109/TPAMI.2006.244).
-
9)
-
17. Sim, T., Baker, S., Bsat, M.: ‘The CMU pose, illumination, and expression database’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (12), pp. 1615–1618 (doi: 10.1109/TPAMI.2003.1251154).
-
10)
-
11)
-
5. Choi, S.-I., Choi, C.-H., Kwak, N.: ‘Face recognition based on 2D images under illumination and pose variations’, Pattern Recognit. Lett., 2011, 32, (4), pp. 561–571 (doi: 10.1016/j.patrec.2010.11.021).
-
12)
-
2. Hwang, W., Kim, W., Suh, S., et al: ‘Face recognition using averaging example image’, Electron. Lett., 2014, 50, (25), pp. 1921–1923 (doi: 10.1049/el.2014.2966).
-
13)
-
1. Zou, X., Kittler, J., Messer, K.: ‘Illumination invariant face recognition: a survey’. IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems, Arlington, Virginia, USA, September, 2007, pp. 1–8.
-
14)
-
12. Liang, J., Yang, S., Winstanley, A.: ‘Invariant optimal feature selection: a distance discriminant and feature ranking based solution’, Pattern Recognit., 2008, 41, (5), pp. 1429–1439 (doi: 10.1016/j.patcog.2007.10.018).
-
15)
-
107. Georghiades, A., Belhumeur, P., Kriegman, D.: ‘From few to many: illumination cone models for face recognition under variable lighting and pose’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, (6), pp. 643–660 (doi: 10.1109/34.927464).
-
16)
-
23. Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: ‘Discriminative common vectors for face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, pp. 4–13 (doi: 10.1109/TPAMI.2005.9).
-
17)
-
1. Castillo, L.E., Cament, L.A., Galdames, F.J., Perez, C.A.: ‘Illumination normalisation method using Kolmogorov-Nagumo-based statistics for face recognition’, Electron. Lett., 2014, 50, (13), pp. 940–942 (doi: 10.1049/el.2014.0513).
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