在這篇論文中，我們主要著重於找出一種將人臉圖片的光線正規劃的方法，希望這樣的方法能夠改善現行人臉辨識演算法的準確率。在人臉辨識的相關研究中，人臉圖片的光線規一化是人臉辨識系統很重要的一個部分，因為很多廣為使用的人臉辨識演算法都很容易受光線的影響，而光線的影響又在我們日常生活中無所不在。因此如何消除取得的人臉圖片中光線的影響是一個很重要的研究課題，已經有很多人提出了相關的研究，但是現行大部分的方法不是只取出不受光線影響的細緻輪廓特徵作辨識，就是對整張影像直接做光線補償，兩種作法都有其缺陷。
因此在這篇論文中我們使用一個比較新的架構，在這樣的架構中人臉圖片中比較粗略的輪廓與比較細緻的特徵將會分開處理。在這樣的架構之下，首先我們必須將人臉圖片分解為比較粗略的輪廓與比較細緻的特徵兩種特徵影像。當人臉圖片被分解完成之後，因為光線主要出現在比較粗略的輪廓影像中，接下來我們必須在人臉的輪廓影像中做光線正規化的處理；而在人臉的細緻特徵部分雖然已經去除大部分光線的影響，還是有些受雜訊影響的小亮點，因此我們需要將人臉的細緻特徵影像做一些簡單的平滑濾波，用來減少這些雜訊的影響。最後我們將處理完的人臉輪廓影像與細緻特徵影像結合在一起，重建出一張光線均勻的人臉圖片。
我們使用Yale B & Extend Yale B人臉資料庫做了一些人臉辨識實驗，總共測試了38個人，每個人有在64種不同光線下拍攝的照片後證明我們的方法不但能夠讓影像在處理過後看起來更清楚，也對於人臉辨識演算法有所幫助。

In the thesis, we focus on how to reconstruct the illumination normalized face image which can be used to improve face recognition result. Here, we employ a framework to process large-scale and small-scale features image independently. In this frame work, first, we decompose the face image into a large-scale feature image and a small-scale feature image. Second, once a face image is decomposed into a large-scale feature image and a small-scale feature image, normalization is then mainly performed on the large-scale feature image. Then, a smooth operator is applied on the small-scale feature image. Finally, we combine the large- and small- scale feature images to generate an illumination normalized face image. We test our method by a face recognition algorithm using Yale B & Extend Yale B database which contains 38 subjects under 64 different illumination conditions. The experimental result shows that image processed by our method not only has better visual quality, but also improves the performance of face recognition.

[1] Y. Adini, Y. Moses and S. Ullman. Face Recognition: The Problem of Compensating for Changes in Illumination Direction. IEEE TPAMI, 19(7):721-732, 1997.
[2] M.Turk and A.Pentland. Eigenfaces for Recognition. Journal of Cognitive Neurosci, 3:71–86, 1991.
[3] M. S. Bartlett, J. R. Movellan and T. J. Sejnowski. Face Recognition by Idenependent Component Analysis. IEEE Transactions on Neural Networks, 13(6):1450-1464, 2002.
[4] X. D. Xie and K. M. Lam. An Efficient Illumination Normalization Method for Face Recognition. Pattern Recognition Letters, 27(6): 609-617, 2006.
[5] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE TPAMI, 19(7): 711–720, 1997.
[6] T. Chen, X. S. Zhou, D. Comaniciu and T.S. Huang. Total Variation Models for Variable Lighting Face Recognition. TPAMI, 28(9):1519-1524, 2006.
[7] H. T. Wang, S. Z. Li and Y. S. Wang. Face Recognition under Varying Lighting Conditions using Self Quotient Image. International Conference on FGR, 819-824, 2004.
[8] W. Wang, J. Song, Z.X. Yang and Z. Chi. Wavelet-Based Illumination Compensation for Face Recognition using Eigenface Method. Proceedings of Intelligent Control and Automation, 2006, Dalian, China.
[9] R. Basri and D. Jacobs. Photometric Stereo with General, Unknown Lighting. IEEE Conference on CVPR, 374–381, 2001.
[10] B. K. P Horn. Shape from Shading ： A Method for Obtaining the Shape of a Smooth Opaque Object From One View. Doctoral Dissertation, Massachusetts Institule of Technology, USA, 1970.
[11] A. Georghiades, P. Belhumeur and D. Kriegman. From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. TPAMI, 23(6):643-660, 2001.
[12] X. D. Xie and K.M. Lam. Face Recognition under Varying Illumination Based on a 2D Aace Shape Model. Journal of Pattern Recognition, 38(2）:221-230, 2005.
[13] Y. K. Hu and Z.F. Wang. A Low-dimensional Lllumination Space Representation of Human Faces for Arbitrary Lighting Conditions. Proceedings of ICPR, Hong Kong, 1147-1150, 2006.
[14] W. L. Chen, E. M. Joo 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, 36(2):458∼466, 2006.
[15] S. H A and R. R T. The Quotient Image: Class-Based Re-rendering and Recognition with Varying Illuminations. TPAMI, 23(2):129-139, 2001.
[16] R. Ramamoorthi. Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object. TPAMI, 24(10):1322-1333, 2002.
[17] R. Gross and V. Brajovic, “An image preprocessing algorithm for illumination invariant face recognition,” 4th Audio- and Video-Based Biometrie Person Authentication, pp. 10–18, 2003.
[18] H. Wang, S. Li, and Y. Wang, “Generalized quotient image,” IEEE CVPR, 2004.
[19] D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE Trans. on Image Processing, vol. 6, pp. 451–452, 1997.
[20] D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. On Image Processing, vol. 6, pp. 965–976, 1997.
[21] Y. K. Park, J. K. Kim, “A new methodology of illumination estimation/normalization based on adaptive smoothing for robust face recognition,” IEEE ICIP 2007, pp 149-152.
[22] W.L. Chen, E.M. Joo 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, 36(2):458∼466, 2006.
[23] K. Chen, “Adaptive smoothing via contextual and local discontinuities,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27(10), pp. 1552–1567, 2005.
[24] P. Saint-Marc, J. S. Chen, and G. Medioni, “Adaptive smoothing: a general tool for early vision,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 13(6), pp. 514–529, 1991.
[25] A.S.Georghiades, P.N. Belhumeur, and D.J. Kriegman, "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", IEEE Trans. Pattern Anal. Mach. Intelligence, 2001.
[26] A. Shashua, and T. Riklin-Raviv, “The quotient image: Class-based re-rendering and recognition with varying illuminations”, Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, pp129-139, 2001.
[27] T. Riklin-Raviv and A. Shashua. “The Quotient image: Class based recognition and synthesis under varying illumination”. Conference on Computer Vision and Pattern Recognition, pages 566–571, Fort Collins, CO, 1999.
[28] R. Ramamoorthi, P. Hanrahan, “On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object”, J. Opt. Soc. Am., Vol. 18, No. 10, 2001.
[29] 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, 2002-10-21.
[30] R. Ramamoorthi and P. Hanrahan, “An Efficient Representation for Irradiance Environment Maps”, SIGGRAPH 01, pages 497–500, 2001.
[31] R. Basri and D. Jacobs, “Lambertian Reflectance and Linear Subspaces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 2, pp. 218-233, 2003.
[32] C.H. Hsu and C.C. Chen, “SVD-Based Projection for Face Recognition”, IEEE EIT 2007 Proceedings, pp. 703-706.
[33] X. Xie1, W.S. Zheng1,3, J. Lai, P. C. Yuen, “Face Illumination Normalization on Large and Small Scale Features”, CVPR 2008 IEEE.