Abstract
This paper presents an enhanced facial preprocessing and feature extraction technique for an illumination-roust face recognition system. Overall, the proposed face recognition system consists of a novel preprocessing descriptor, a differential two-dimensional principal component analysis technique, and a fusion module as sequential steps. In particular, the proposed system additionally introduces an enhanced center-symmetric local binary pattern as preprocessing descriptor to achieve performance improvement. To verify the proposed system, performance evaluation was carried out using various binary pattern descriptors and recognition algorithms on the extended Yale B database. As a result, the proposed system showed the best recognition accuracy of 99.03% compared to other approaches, and we confirmed that the proposed approach is effective for consumer applications.
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References
Zuo, F., de With, P.H.N.: Real-time embedded face recognition for smart home. IEEE Trans. Consum. Electron. 51(1), 183–190 (2005)
Zhao, W., Chellappa, R., Phillips, R.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)
Abate, A.F., Nappi, M., Riccio, D., Riccio, G.: 2D and 3D face recognition: A survey. Pattern Recognit. Lett. 28(14), 1885–1906 (2007)
Chen, W., Er, M.J., Wu, S.: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans. Syst. Man Cybern. Part B-Cybern. 36(2), 458–466 (2006)
Ruiz-del-Solar, J., Quinteros, J.: Illumination compensation and normalization in eigenspace-based face recognition: A comparative study of different pre-processing approaches. Pattern Recognit. Lett. 29(14), 1966–1979 (2008)
Hsieh, P.C., Tung, P.C.: Illumination-robust face recognition using an efficient mirror technique. In: International Congress on Image and Signal Processing, pp. 1–5 (2009)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Zhang, W., Shan, S., Chen, X., Gao, W.: Local Gabor binary patterns based on mutual information for face recognition. Int. J. Image Graph. 7(4), 777–793 (2007)
Fu, X., Wei, W.: Centralized binary patterns embedded with image Euclidean distance for facial expression recognition. In: International Conference of Neural Computation, vol. 4, pp. 115–119 (2008)
Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with center-symmetric local binary patterns. In: Kalra, P.K., Peleg, S. (eds.) ICVGIP 2006. LNCS, vol. 4338, pp. 58–69. Springer, Heidelberg (2006)
Jian, Y., David, Z., Alejandro, F., Yang, J.Y.: Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
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. 23(6), 643–660 (2001)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class-specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
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Kim, DJ., Sohn, MK., Kim, H., Ryu, N. (2014). Enhanced Face Preprocessing and Feature Extraction Methods Robust to Illumination Variation. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_23
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DOI: https://doi.org/10.1007/978-3-319-11289-3_23
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