近幾年來，由於安全上的需求，所以利用人臉來進行身份辨識的應用越來越廣泛，在許多從事人臉辨識的研究的文獻中，常利用人臉影像擷取出來的特徵，來分辨出不同的人。然而在實際的應用上，常常會因為環境中光源的不均勻照射，使得同一張人臉會有很大的不同，因而導致人臉的辨識率大幅下降，為了提昇辨識效能，我們提出一個區域對比增強的方法，可以有效的解決人臉辨識在不同光源下的改變。

本篇論文提出的人臉辨識的演算法，則是在辨識前對影像做離散餘弦轉換，取出人臉影像的低頻部份，有效降低影像的維度，因此在辨識的時間上也會相對的減少，最後交給支持向量機（SVM），來決定辨識的結果。本論文測試的人臉資料庫為Yale_B，經使用支持向量機的辨識率可達99.13%，在已發表的論文中是辨識較好的方法之一。

In recent years, many face recognition algorithms have been developed for surveillance systems and promising results have been reported in specific environments. The human face recognition highly relies on extracted stable features from input images. In practical application environments, however, the direction of the illuminant is uncontrollable and it will result in unstable feature extraction. For remedying the problems caused by non-uniform light sources, illumination compensation is necessary.
In this thesis, we propose a local contrast enhancement approach to reduce the effect of non-uniform light sources, and integrate it with a face recognition system. Through the process of local contrast enhancement, the facture extraction based on digital cosine transformation (DCT) becomes more reliable. The adopted classification kernel is support vector machines (SVM) which has been shown to be a robust classifier. The well-known human face database Yale_B is used for verifying system performance, and the recognition rate can achieve to 99.13%. As far as we known, the recognition rate is better than all of the published literatures.

[1] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, 「Face Recognition: A Literature Survey,」 ACM Computing Surveys, Vol. 35, No. 4, pp. 399-458, Dec. 2003.
[2] S. Z. Li and J. Lu, 「Face Recognition Using the Nearest Feature Line Method,」 IEEE Transactions on Neural Networks, Vol. 10, No. 2, March. 1999.
[3] R. Brunelli and T. Poggio, 「Face Recognition: Features versus Templates,」 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15, No.10, pp. 1042-1052, Oct. 1993.
[4] M. J. Er, S. Wu, J. Lu, and H. L. Toh, 「Face Recognition With Radial Basis Function (RBF) Neural Networks,」 IEEE Transactions on Neural Networks, Vol.13 No. 3, pp.697-710, May. 2002.
[5] M. Bicego, G. Iacono, and V. Murino, 「Face Recognition with Multilevel B-Splines and Support Vector Machines,」 ACM WBMA March. 2003.
[6] J.T. Kwok, 「Moderating the outputs of support vector machine classifiers,」 IEEE Trans. on Neural Networks, vol. 10, pp. 1018-1031, Sep. 1999.
[7] M.A. Turk and A.P. Pentland, 「Face recognition using eigenfaces,」 IEEE Proceeding on Computer Vision and Pattern Recognition, pp. 586-591, June. 1991.
[8] 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, pp. 711-720, July. 1997.
[9] L.I Smith, 「A tutorial on Principal Components Analysis,」 Feb. 2002.
[10] K. I. Kim, K. Jung and J. Kim, 「Face Recognition Using Kernel Principal Component Analysis,」 IEEE Signal Processing Letters, Vol. 9, pp.40-42, Feb. 2002,
[11] B. Heisele, P. Ho, J. Wu, and T. Poggio, 「Face recognition: component-based versus global approaches,」 Computer Vision and Image Understanding, pp.6–21, Feb. 2003.
[12] X.Y Jing and D. Zhang, 「A face and palmprint recognition approach based on discriminant DCT feature extraction,」 IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol.34, pp.2405-2415, Dec. 2004.
[13] M.J. Er, W. Chen, and S. Wu, 「High-Speed Face Recognition Based on Discrete Cosine Transform and RBF Neural Networks,」 IEEE TRANSACTIONS ON NEURAL NETWORKS, Vol. 16, pp.679-691, May. 2005.
[14] W. Chen, M.J. Er and S. Wu, 「Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain,」 IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol.36, pp.458-466, Apr. 2006.
[15] H. Cevikalp, M. Neamtu, M. Wilkes and A. Barkana, 「Discriminative common vectors for face recognition,」 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 27, pp. 4 - 13, Jan. 2005.
[16] J. Yang, D. Zhang, A.F. Frangi, and J.J. Yang, 「Two-Dimensional PCA: A New Approach to Representation and Recognition,」 IEEE Transactions on pattern analysis and machine intelligence, pp.131-137, Jan. 2004.
[17] A. M. Martinez, A. C. Kak, 「PCA versus LDA,」, IEEE Trans. Pattern Anal. Machine Intell., vol. 23, no. 2, pp. 228-233, Feb. 2001.
[18] X. He, S. Yan, Y. Hu, P. Niyogi, H. J. Zhang, 「Face recognition using laplacianfaces,」 IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 3, pp. 328-340, March 2005.
[19] B. G. Park, K. M. Lee, S. U. Lee, 「Face recognition using face-ARG matching,」 IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 12, pp. 1982-1988, Dec. 2005.
[20] K. Venkataramani, S. Qidwai, B. V. K. Vijayakumar, 「Face authentication from cell phone camera images with illumination and temporal variations,」 IEEE Trans. System, Man, and Cybernetics, vol. 35, no. 3. pp. 411-418, Aug. 2005.
[21] J. Ruiz-del-Solar, P. Navarrete, 「Eigenspace-based face recognition: a comparative study of different approaches,」 IEEE Trans. System, Man, Cybernetics, vol. 35, no. 3, pp. 315-325, Aug. 2005.
[22] J. Kim, J. Choi, J. Yi, M. Turk, 「Effective representation using ICA for face recognition robust to local distortion and partial occlusion,」 IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 12, pp. 1977-1981, Dec. 2005.
[23] M. Turk and A. Pentland, 「Eigenfaces for recognition,」 J. Cogn. Neur osci.,vol. 3, no. 1, pp. 71–86, 1991.
[24] H. Yu and J. Yang, 「A direct LDA algorithm for high-dimensional data—With application to face recognition,」 Pattern Recognition, vol. 34, no. 10, pp. 2067–2070, 2001.
[25] J. Huang, P. C. Yuen, W. S. Chen, and J. H. Lai, 「Component-based subspace LDA method for face recognition with one training sample,」 Opt. Eng., vol. 44, no. 5, p. 057 002, 2005.
[26] B. Scholkopf, A. Smola, and K.Muller, 「Nonlinear component analysis as a kernel eigenvalue problem,」 MPI fur biologische kybernetik, Tubingen, Germany, Tech. Rep. 44, 1996.
[27] J. W. Lu, K. Plataniotis, and A. N. Venetsanopoulos, 「Face recognition using kernel direct discriminant analysis algorithms,」 IEEE Trans. Neural Network, vol. 14, no. 1, pp. 117–126, Jan. 2003.
[28] G. Baudat and F. Anouar, 「Generalized discriminant analysis using a kernel approach,」 Neural Computer, vol. 12, no. 10, pp. 2385–2404, 2000.
[29] J.H. Pong, C. Yuen, W.S. Chen and J.H. Lai, 「Choosing Parameters of Kernel Subspace LDA for Recognition of Face Images Under Pose and Illumination Variations,」 IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol.37, no. 37, pp.847-862, Aug. 2007.
[30] W.S. Chen, C. Pong, Y.J. Huang and D.Q. Dai, 「Kernel Machine-Based One-Parameter Regularized Fisher Discriminant Method for Face Recognition, 」 IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol.35, no. 5, pp.659-669, Aug. 2005.