人臉辨識是生物測定學最重要的技術之一。這篇論文主要的目標有兩個，第一是發展一個穩固的影像特徵擷取系統，第二是提出一個可做到抗扭曲人臉辨識的方法。
我們提出了一個影像特徵擷取的系統，之後可以用在人臉的身份辨識和表情辨識。首先對影像作Gabor的小波轉換作為人臉影像的特徵，然後依序用PCA、LDA做特徵的降維，最後即用KNN在低維度空間做人臉的身份或表情辨識。另外，我們也嘗試以可塑性網格的方法，去克服人臉辨識系統中影像的扭曲。最後報告一些兩個方法的實驗結果和討論。

Face Recognition is one of the most important technology of biometrics. The purposes of this thesis are to develop a robust face image retrieval system and propose a method to accomplish face recognition whose images may suffer from some deformations.
A scheme of face image retrieval is proposed for face recognition and facial expression recognition. We utilize a 2D Gabor wavelet representation to be an alternative form of face image. After feature extraction by Gabor Wavelet Transform, two dimensionality reduction methods - PCA and LDA - are sequentially applied on the feature vector. At last, we use a KNN classifier to recognize face image and facial expression in lower dimensional space, respectively. And we also try another approach, which is so called elastic graph matching or dynamic link architecture, to conquer the image deformations of the face recognition system. Finally, some results and discussions are reported between the two approaches.

[Alpa2010] E. Alpaydin, Introduction to Machine Learning, 2nd ed., MIT Press, 2010.
[Chen2008] C.C. Chen, Y.S. Shieh, and H.T. Chu, “Face Image Retrieval by Projection-Based Features,” The 3rd International Workshop on Image Media Quality and its Applications, Kyoto, 58-62, 2008.
[Daug1985] J.G. Daugman, “Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters,” J. Optical Soc. Am. A, Vol. 2, 1160-1169, 1985.
[Lade1993] M. Lades, J.C. Vorbrüggen, J. Buhmann, J. Lange, C. von der Malsburg, R.P. Würtz, and W. Konen, “Distortion Invariant Object Recognition in the Dynamic Link Architecture,” IEEE Transactions on Computers, Vol. 42, No. 3, 300-311, 1999.
[Lyon1998] M. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, “Coding Facial Expressions with Gabor Wavelets,” Proceedings, The 3rd IEEE International Conference on Automatic Face and Gesture Recognition, Nara Japan, 200-205, 1998.
[Lyon1999] M.J. Lyons, J. Budynek, and S. Akamatsu, “Automatic Classification of Single Facial Images,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 21, No. 12, 1357-1362, 1999.
[Sato2012a] Y.D. Sato and Y. Kuriya, “Efficient and Effective Gabor Feature Representation for Face Detection,” World Academy of Science, Engineering and Technology, 2012.
[Sato2012b] Y.D. Sato and Y. Kuriya, “Elastic Graph Matching on Gabor Feature Representation at Low Image Resolution,” ICANN, 387-394, 2012.
[Web01] http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm, last access on Mar 31, 2013.
[Wisk1997] L. Wiskott, J.M. Fellous, N. Krüger, and C. von der Malsburg, “Face Recognition by Elastic Bunch Graph Matching,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 775-779, 1997.
[Zhao2003] W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, “Face Recognition : A Literature Survey,” ACM Computing Surveys, Vol. 35, No. 4, 399–458, 2003.