在人臉辨識的相關研究中，所要加強和克服的不外乎辨識的準確率，而輸入影像的前處理步驟及輸入影像的內容也將明顯影響辨識的準確性。所謂影像的內容指的是拍攝環境和人臉上的光線變化，這個因素影響辨識率很深。之前許多的研究都是拿整張人臉去做辨識。在本論文中是將人臉分成不同的區域，然後分別將這些區域去做辨識。因為整張人臉包含了許多膚色區域，而這些膚色區域對於表示人的特徵上並沒有太大的幫助，反而會因為光線或環境的變化去影響這些膚色區域，所以為了能減少這些膚色區域對辨識的影響，所以才提出了在人臉上框取幾個不同的區域，這些區域要以膚色愈少愈好。
人臉辨識的演算法則是LDA + PCA ，而在辨識前我們必須對辨識影像先做小波轉換，原因是小波轉換能將影像保留最不變性的部份，也就是會去除掉一些不必要的部分，另外影像也會縮小，在辨識的時間上也就減少。在做完辨識後因為框取了數個辨識區域所以會有數個辨識結果，因此在整合這些辨識結果方面一般來說通常是採用投票法，但是在本論文中除了採用投票法還加上權重。所謂的權重是依據特徵做分群後的結果而定。當投票法無法判定時便採用以權重來辨識。最後經由自行拍攝的影像測試結果顯示，我們所提出的方法在辨識率上的確比使用整張人臉來的要好。

The relevant research of face recognition has have to be strengthen and overcome rate of accuracy of recognition. The previous treatment steps and content of input image obviously affect accuracy of recognition. The so-called content of image means that the environment and the changes of lighting on face, and this factor affects the rate of recognition very deeply. Most previous research focus on taking whole face image for recognition. This thesis focuses on dividing the face image into different areas, then takes these different areas to recognize. The face image is often full of skin areas, which have less help to recognition but damage. Furthermore, the color of skin areas is easily influenced by the lighting condition or changes of the environment. In order to prevent the above drawbacks which may decrease the accuracy of face recognition, the paper propose a method which fetches different areas of the face as features and these areas have little skin color areas .
The algorithm of face recognition consists of LDA + PCA and before recognition we must first do wavelet transform on the image. The reason doing wavelet transform is to keep the changeless parts of image, in other words it can remove some unnecessary parts of image. In addition, the size of image will dwindle too, so the time to recognize will be reduced. After finishing of recognition, due to several areas to recognize, several results of recognition will be produced. So generally speaking in combining these result of recognition, people usually adopts the vote method, but in addition to voting method, we also adopted weighting approach in this thesis. The weight value was based on the results of features which were clustered. We adopt weighting method when the vote method is unable to decide. The experimental results showed that our proposed approach which is based on certain blocks in the face is better than other methods which using the entire face image in accuracy rate.

[1] L. I Smith, “A tutorial on Principal Components Analysis”, 2002 .
[2] Y. Nara, J. Yang, Y. Suematsu, “Face recognition using improved principal component analysis”, Proc. International Symposium on Micromechatronics and Human Science, pp. 77 - 82, 2003 .
[3] X. Wang, X. Tang, “Random sampling LDA for face recognition”, Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 259 - 265, 2004 .
[4] X. He , S. Yan, Y. Hu, P. Niyogi, H. J. Zhang, “Face recognition using Laplacianfaces”, IEEE Transactions on Pattern Analysis and Machine Intelliqence, Vol. 27, No. 3, 2005 .
[5] J. Baek, M. Kim “Face recognition using partial least squares component”, Pattern Recognition 37, 1303 - 1306, 2004 .
[6] J. T. Chien, C. C. Wu, “Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 12, 2002 .
[7] V. Perlibakas, “Distance measures for PCA-based face recognition”, Pattern Recognition Letters 25, 711 - 724, 2004 .
[8] Byung-Joo Oh, “Face recognition by using neural network classifiers based on PCA and LDA”, IEEE International Conference on Systems, Man and Cybernetics, Vol. 2, pp. 1699 - 1703, 2005.
[9] M. Doi, K. Sato, K. Chihara, “A Robust Face Identification against Lighting Fluctuation for Lock ”, IEEE International Conference on Automatic Face and Gesture Recognition, pp. 42 - 47, 1998.
[10] S. D. Wei, S. H. Lai, “Robust Face Recognition under Lighting Variations”, Proc. International Conference on Pattern Recognition, Vol. 1, pp. 354 - 357, 2004 .
[11] X. Xie, K. M. Lam, “An Efficient Method for Face Recognition under Varying Illumination”, IEEE International Symposium on Circuits and Systems, Vol. 4, pp. 3841 - 3844, 2005 .
[12] K. Nishino, P. N. Belhumeu, S. K. Nayar, “Using eye reflections for face recognition under varying illumination”, IEEE International Conference on Computer Vision, Vol. 1, pp. 519 - 526, 2005 .
[13] R. C. Gonzalez, R. E .Woods, 繆紹綱編譯, “Digital Image Processing 2/e”, 台灣培生教育出版股份有限公司出版, 普林斯頓國際有限公司發行 .
[14] H. Wang, S. Z. Li, Y. Wang, “Face recognition under varying lighting conditions using self quotient image”, IEEE International Conference on Automatic Face and Gesture Recognition, pp. 819 - 824, 2004 .
[15] H. Wang, S. Z. Li, Y. Wang , J. Zhang, “Self quotient image for face recognition”, International Conference on Image Processing, Vol 2, pp. 1397 - 1400, 2004 .
[16] Z. H. Zhou, X. Geng, “Projection functions for eye detection”, Pattern Recognition 37, 1049 - 1056, 2004.
[17] H. Cevikalp, M. Neamtu, M. Wilkes, A. Barkana, “Discriminative common vectors for face recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 27, pp. 4 - 13, 2005.
[18] H.Cevikalp, M. Wilkes, “Face recognition by using discriminative common vectors”, Proc. International Conference on Pattern Recognition Vol. 1, pp. 326 - 329, 2004.
[19] J. T. Tou, R. C. Gonzalez, Pattern Recognition Principles, Addison-Wesley Publishing Company, 1974.