Abstract
We present an adaptive normalization method based robust face recognition which is sufficiently insensitive to such illumination variations. The proposed method takes advantage of the concept of situation-aware construction and classifier fusion. Most previous face recognition schemes define their system structures at their design phases, and the structures are not adaptive during run-time. The proposed scheme can adapt itself to changing environment illumination by situational awareness. It processes the adaptive local histogram equalization, generates an adaptive feature vectors for constructing multiple classifiers in accordance with the identified illumination condition. The superiority of the proposed system is shown using ’Yale dataset B’, IT Lab., FERET fafb database, where face images are exposed to wide range of illumination variation.
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References
Diamantaras, K.I., Kung, S.Y.: Principle Component Neural Networks: Theory and Application. John Wiley and Sons, Chichester (1996)
Caselles, V., Lisani, J.-L., Morel, J.-M., Sapiro, G.: Shape preserving local histogram modification. IEEE Transactions on Image Processing 8(2), 220–230 (1999)
Swets, D., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. on PAMI 18(8), 831–836 (1996)
Kuncheva, L.I., Bezdek, J.C., Duin, R.P.W.: Decision Templates for Multiple Classier Fusion: An Experimental Comparison. Pattern Recognition 34(2), 299–314 (2001)
Donato, G., Bartlett, M., Hager, J., Ekman, P., Sejnowski, I.: Classifying facial actions. IEEE Trans. on PAMI 21(10), 974–989 (1999)
Potzsch, M., Kruger, N., Von der Malsburg, C.: Improving Object recognition by Transforming Gabor Filter reponses. Network: Computation in Neural Systems 7(2), 341–347
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few to Many: Illumination Cone Models for face recognition under Variable Lighting and Pose. IEEE Trans. on PAMI 23(6), 643–660 (2001)
Georghiades, A., Kriegman, D., Belhumeur, P.N.: Illumination Cones for Recognition Under Variable Lighting: Faces. In: Proc. IEEE Conf. CVPR, pp. 52–58 (1998)
Liu, H., et al.: Illumination Compensation and Feedback of Illumination Feature in Face Detection. In: Proc. International Conferences on Information-technology and Information-net, Beijing, vol. 3, pp. 444–449 (2001)
Bossmaier, T.R.J.: Efficient image representation by Gabor functions - an information theory approach. In: Kulikowsji, J.J., Dicknson, C.M., Murray, I.J. (eds.), pp. 698–704. Pergamon Press, Oxford
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Goldberg, D.: Genetic Algorithm in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Publishing Company, Reading (1993)
Field, D.: Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Amer. A 4(12), 2379–2394 (1987)
Faugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimization by two-dimensional cortical filters. Journal Opt. Soc. Amer. 2(7), 675–676 (1985)
Zhao, W., Chellappa, R.: Robust Image-Based 3D Face Recognition, CAR-TR-932, N00014-95-1-0521, CS-TR- 4091, Center for Auto Research, UMD (2000)
Gao, W., Shan, S., Chai, X., Fu, X.: Virtual Face Image Generation for Illumination and Pose Insensitive Face Recognition. In: Proc. of ICASSP 2003, HongKong, vol. IV, pp. 776–779 (2003)
Murase, H., Nayar, S.: Visual Learning and recognition of 3D object from appearance. IJCV 14, 5–24 (1995)
Shashua, A., Riklin-Raviv, T.: The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations. IEEE Trans. on PAMI, 129–139 (2001)
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few to Many: Illumination Cone Models for Face Recognition under Differing Pose and Lighting. IEEE TPAMI 23(6), 643–660 (2001)
Yang, J., Liu, L., Jiang, T., Fan, Y.: A modified Gabor filter design method for fingerprint image enhancement. Pattern Recognition Letters archive 24(12), 1805–1817 (2003) ISSN:0167-8655
Shan, S., Gao, W., Cao, B., Zhao, D.: Illumination normalization for robust face recognition against varying lighting conditions. In: AMFG 2003 IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003, pp. 157–164 (2003)
Kondo, T., Yan, H.: Automatic human face detection and recognition under non-uniform illumination. Pattern Recognition 32(10), 1707–1718 (1999)
Liu, C., Wechsler, H.: Evolutionary persuit and its application to face recognition. IEEE Trans. on PAMI 22(6), 570–582 (2000)
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Rhee, P.K., Jeon, I., Jeong, E. (2005). Adaptive Normalization Based Highly Efficient Face Recognition Under Uneven Environments. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_107
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DOI: https://doi.org/10.1007/11539117_107
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
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