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標題: 改善傅立葉分析在人臉識別上做陰影補償的應用
Improving the Shadow Compensation Using Fourier Analysis With Application to Face Recognition
作者: 蕭淑美
Shiau, Shu-Mei
關鍵字: Face recognition;人臉識別;Fourier transforms;illumination variation;illumination direction;shadow compensation;傅立葉轉換;照明變化;光照方向;陰影補償
出版社: 通訊工程研究所
引用: [1]R. C.Gonzalez. , R. E.Woods and S. L.Eddins , 繆紹綱 譯,Digital Image Processing Using MATLAB,1st Edition數位影像處理-運用MATLAB,台灣培生教育出版,初版一刷,2008。 [2]Gonzalez,Woods , 繆紹綱 譯,Digital Image Processing 3/e, 數位影像處理台灣培生教育出版,初版三刷,2009。 [3]何承諭,“三維離散X射線轉換實現之改良 ”,國立中興大學碩士論文,2011。 [4]連國珍 著作,數位影像處理,儒林圖書,五版一刷,2008。 [5]盧俊良,“基於光線與臉部表情變化之下人臉辨識”,國立中央大學資訊工程研究所,2009。 [6]S.I. Choi , and G.M. Jeong,“Shadow Compensation Using Fourier Analysis With Application to Face Recognition,”IEEE Signal Process. Lett.,vol. 18, pp.23-26,2011. [7]X. Zou, J. Kittler, and K. Messer, “Illumination invariant face recognition: Asurvey,” in IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems, pp. 1–8, 2007. [8]R. Javier and Q. Julio, “Illumination compensation and normalization in eigenspace-based face recognition: A comparative study of different pre-processing approaches,” Pattern Recognit. Lett. , vol. 29, pp.1966–1979, 2008. [9]T. Zhang, Y. Y. Tang, B. Fang, Z. Shang, and X. Liu, “Face recognition under varying illumination using gradient faces,” IEEE Trans. Image Process., vol. 18, pp. 2599–2606, 2009. [10]A.S. Georghiades and P. N. Belhumeur, “Frome few to many: Illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, pp.643–660, 2001. [11]R..Ramamoorthi, “Analytic PCA construction for theoretical analysis of lighting variability in images of a Lambertian object,”IEEE Trans.Pattern Anal. Mach. Intell., vol. 24, pp. 1322–1333, 2002. [12]S.-I. Choi and C.-H. Choi, “An effective face recognition under illumination and pose variations,” in Proc. Int. Joint Conf. Neural Networks, pp. 914–919,2007. [13]H.Wang, S. Li, and Y.Wang, “Face recognition under varying lighting conditions using self quotient image,” in Proc. IEEE Int. Conf.Automatic Face and Gesture Recognition, pp. 819–824, 2004. [14]X. Xie and K.-M. Lam, “An efficient illumination normalization method for face recognition, ”Pattern Recognit . Lett., vol. 27, pp.609–617, 2006. [15]T. Ahonen , A. Hadid , and M. Pietikainen , “Face description with localbinary patterns: Application to face recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 28, pp. 2037–2041, 2006. [16]A.V. Oppenheim, A. S.Willsky , and S. H. Nawab , Signal and Systems,2nd ed. Upper Saddle River, NJ: Prentice-Hall, pp. 303–304, 1996. [17]R. C. Gonzales and R. E.Woods , Digital Image Processing, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 2002.

Shadows that occur on face images due to illumination variation can change the appearance of a face and degrade face recognition performance. In this thesis, we implement and improve a new shadow compensation method based on the Fourier analysis for handling illumination variation.

First, we classify the original images into three categories according to its illumination direction: (1) illumination from left, (2) uniform illumination, and (3) illumination from right. The classified images are then further classified into two sub-categories according to the area on the face that is covered by shadow: (1) 1/2 area and (2) 2/3 area.

Second, after Fourier transform, we adjust the proportion between the auxiliary magnitude and the magnitude of the original image according to the classified category. The magnitude spectrum of the restored image is the sum of the auxiliary magnitude and the magnitude of the original image and its phase is the original phase components.

The proposed method improved the shadow compensation as compared to the previous approach because of its adaptability on illumination direction. The experimental results and analysis show that the performance of our proposed method is indeed better than the original method significantly.
其他識別: U0005-0207201211411100
Appears in Collections:通訊工程研究所

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