臺灣博碩士論文加值系統

本論文最主要是研究及探討，人的臉部器官中眼睛的辨識。為什麼要辨識人臉器官中的眼睛呢？因為人的眼睛可以傳達很多的訊息，當人累的快睡著的時候，眼睛會不自覺的闔上；而當你精神飽滿的時候眼睛就會比平常時還要大。如果我們能把眼睛辨識的方法應用在駕駛人身上，我們就可以做一個自動提醒駕駛人的安全系統；或者應用在辨識人的身份。因為只要我們能夠建立一個公司員工的臉部影像的資料庫，那我們在辨識一個人的身份時，臉部的五官就非常重要了；尤其眼睛是靈魂之窗，每個人的眼睛在臉的什麼位置、什麼形狀跟大小，我們都可以依據這些資訊來判斷這個人的身份。 然而做眼睛檢測的方法有許多，比如：1、粗略輪廓預測副程式RCER(Rough Contour Estimation Routine)、數學型態學(mathematical morphology)配合變形樣本模型(deformable template model) 這些影像處理的技術。2、邊緣搜尋的技術。然後利用這些技術來找出眼睛的位置跟形狀，並比較彼此之間的準確度。在本篇論文中，最主要是評估本論文所提及檢測眼睛的形狀及大小的方法，並作之間的優劣比較與說明。

This thesis is towards the research of detecting people's eyes in the face. People's eyes contain transmit and rich information. When we are tired and sleepy, eyes will not be getting conscientious. On the other hand, Eyes will be also large when you are energetic. If we can watch driver's eyes, We can design a driving security system reminding the driver automatically. Eyes are also important to distinguish people's identity. If we can set up the face image database of the staff of a company, Then we can distinguish a person's identity by one's facial features. Especially eyes are the window of the soul, how big and where in the face, and iris are crucial to determine the identity of a person. There are many methods to detect eyes in a face, For example, 1, The rough eye outline prediction by RCER (Rough Contour Estimation Routine), mathematical morphology and deformable template model; 2, edge detection technologies. We make use of these technologies to find the position and shape of eyes, and then the accuracy of each method is computed. This thesis aims at finding out an efficient method to detect the eyes and their shapes. Comparison among these methods are made. Furthermore, advantages and disadvantages of these methods are finally shown and noted.

中文提要 摘要…………………………………………………… i
英文提要 ABSTRACT……………………………………………… ii
誌謝 ACKNOWLEDGEMENTS…………………………………… iii
目錄 CONTENTS……………………………………………… iv
表目錄 LIST OF TABLES……………………………………… v
圖目錄 LIST OF FIGURES……………………………………… vi
符號說明 LIST OF NOTATIONS…………………………………… vii
一、 緒論…………………………………………………… 1
1.1 研究背景……………………………………………… 1
1.2 論文大綱……………………………………………… 2
1.3 研究理論大綱………………………………………… 4
二、 基礎概念 (研究內容與方法)……………………… 7
2.1 數學形態學 (Morphology)………………………… 7
2.1.1 Dilation and Erosion……………………………… 8
2.1.2 Opening and Closing………………………………… 10
2.1.3 Extensions to Gray-Scale Images………………… 14
2.2. RCER (rough contour estimation routine) 粗略輪廓預測副程式…………………………………………
15
2.2.1 連續物件區域 (Contiguous Object Region Finding) ……
15
2.3 邊緣檢測的基本概念………………………………… 20
三、 理論：研究內容與方法……………………………… 23
3.1 變形樣本模型 (deformable template model) ……… 23
3.2 邊緣搜尋 (Edge detection)……………………… 28
3.2.1 Roberts 的運算方法………………………………… 28
3.2.2 Sobel、Prewitt的邊緣搜尋演算法………………… 30
3.2.3 Frei Chen 的邊緣搜尋演算法……………………… 33
3.2.4 Canny 的邊緣搜尋演算法…………………………… 36
四、 實驗部份Simulation Results……………………… 46
4.1 臉部影像的資料庫(Database of the Face Images)與實驗步驟……………………………………………
46
4.2 實驗結果(Recognition Results) ………………… 65
五、 結論(Conclusion) ………………………………… 66
參考文獻(REFERENCE) ……………………………… 68

[1] C. Darwin, The Expression of the Emotions in Man and Animals, London: John Murray, 1872.
[2] P. Ekman and W. V. Friesen, The Facial Action Coding System, San Francisco, CA: Consulting Psychologist Press,1978.
[3] P. Ekman and W. V. Friesen, Unmasking the Face, Englewood Cliffs, New Jersey: Prentice-Hall,1975.
[4] S. Morishima and H. Harashima,“Emotion space for analysis and synthesis of facial expression,”in Proc. 2nd IEEE Int. Workshop on Robot and Human Communication, pp. 188-193, 1993.
[5] K. Mase,“Recognition of facial expression from optical flow,”IEICE Trans, pp. 3474-3483, 1991.
[6] H. Kobayashi and F. Hara,“The recognition of basic facial expressions by neural network,”in Proc Int. Joint Conf. on Neural Network, pp. 460-466, 1991.
[7] H. Kobayashi and F. Hara, “Recognition of six basic facial expressions and their strength by neural network,”in Proc IEEE Int. Workshop on Robot and Human Communication, pp. 381-386, 1992.
[8] H. Kobayashi and F. Hara,“Recognition of mixed facial expressions by neural network,”in Proc IEEE Int. Workshop on Robot and Human Communication, pp. 387-391,1992.
[9] H. Kobayashi and F. Hara, “Analysis of the neural network recognition characteristics of six basic facial expressions,”in IEEE Int. Workshop on Robot and Human Communication, pp. 222-227,1994.
[10] M. Rosenblum, Y. Yacoob, and L. S. Davis,“Human expression recognition from motion using a radial basis function network architecture,”IEEE Trans. on Neural Networks, vol. 7,no. 5,pp. 1121-1138,1996.
[11] A. L. Yuille, P. W. Hallinan, and D. S. Cohen,“Feature extraction from faces using deformable templates,”In Proc. IEEE Computer Soc. Conf. on Computer Vision and Patt. Recog, pp. 104-109, 1989.
[12] A. L. Yuille, P. W. Hallinan, and D. S. Cohen,“Feature extraction from faces using deformable templates,”Int. J. Compt. Vision, pp. 99-111, 1992.
[13] M. A. Shackleton and W. J. Welsh, “Classification of facial features for recognition,”in Proc.CVPR, pp. 573-579, 1991.
[14] X. Xie, R. Sudhakar and H. Zhuang,“On improving eye feature extraction using deformable templates,”Pattern Recognition, vol. 27, no. 6, pp. 791-799, 1994.
[15] J.Y. Chang, J.L. Chen “A facial expression recognition system using neural networks,” in Proc of International Joint Conference on neural networks, Vol. 5, pp. 3511-3516, 1999.
[16] C. W. Chen, “Human face recognition using deformable template and active contour,” Master Thesis, National Tsing-Hua University, Hsin-Chu, Taiwan, R.O.C., Jun 1991.
[17] T. Poggio, H. Voorhees and A.Yuille, “A Regularized Solution to Edge Detection, ” Tech. Rep. MA, Rep. AIM-833, MIT Artificial Intell. Lab., May 1985.
[18] S. Sarkar and K. L. Boyer,“On Optimal Infinite Impulse Response Edge Detection Filter,”IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 13, no. 11, pp. 1154-1170, Nov 1991.
[19] H. Moon, R. Chellappa and A. Rosenfeld, “Optimal Edge-Based Shape Detection,” IEEE Trans. On Image Processing, Vol.11, no.11, pp. 1209-1227, Nov. 2002.
[20] J.S.Huang and D.H.Tseng, “Statistical Theory of Edge Detection, ”Computer Vision, Graphics, And Image Processing Vol.43, pp. 337-346, 1988.
[21] Julez, B.,“A Method of Coding TV Signals Based on Edge Detection,” Bell System Tech. Vol. 38, No. 4, pp.1001-1020. July 1959,
[22] D.H. Marimont and Y. Rubner,“A Probabilistic Framework for Edge Detection and Scale Selection,”Computer Vision, 1998. Sixth International Conference on , pp. 207-214, Jan 1998.
[23] 程正興,「小波分析演算法與應用」,西安交通常大學出版社,1997.
[24] 程正興，林勇平，「小波分析在圖像科學中的應用」，工程數學學報, 第１８卷「小波專刊」pp. 57-86, 2001.
[25] W.Y. Ma and B.S. Manjunath, “A Technique for Boundary Detection and Image Segmentation,” IEEE Trans. Image Processing, Vol. 9, No.8, pp. 1375-1388, Aug 2000.
[26] 王宇生等, 「一種基於積分變換的邊緣檢測方法」,中國圖形圖像學報,Vol.7(A), No.2, pp. 145-149, 2002.
[27] C. W. Chen,“Human face recognition using deformable template and active contour,” Master Thesis, National Tsing-Hua University, Hsin Chu, Taiwan, R.O.C., Jun 1991.
[28] S. Sarkar and K. L.Boyer,“On Optimal Infinite Impulse Response Edge Detection Filter,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.13, no. 11, pp.1154-1170, Nov 1991.
[29] Sheng Tang, Survey of Edge Detection,
http:// www.cs.unr.edu/~tang_s/research/image/surv.p
[30] Robert M.Haralick, FELLOW,“Digital Step Edges from Zero Crossing of Second Directional Derivatives, ”IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. PAMI-6, no. 1, pp.58-68, JAN 1984.
[31] R.Kirsch, “Computer Determination of the Constituent Structure of Biological Images,” Computer and Biomedical Research, Vol.18, pp. 113-125, Jan. 1971.
[32] L.Sobel, “Camera Models and Machine Perception,” PhD theses, Stanford University, Standford, CA, 1970.
[33] J.Prewitt, “Object Enhancement and Extraction,”Picture Process. Psychopict, pp.75–149, 1970.
[34] Robinson, J.A. “Efficient general-purpose image compression with binary tree predictive coding, ” IEEE Transactions on Image Processing, Vol. 6, No.4, Apr 1997.
[35] W.Frei and C.Chen, “Fast Boundary Detection: A Generalization and a New Algorithm,” IEEE Transactions On Electronic Computers, Vol. C-26, Oct. 1977
[36] John Canny, Member, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No.1, pp. 679-697, Nov 1986.
[37] Julez, B.,“A Method of Coding TV Signals Based on Edge Detection,”Bell System Tech. Vol.38, No.4, pp.1001-1020. July 1959.
[38] D.C.Marr and E.Hildreth, “Theory of Edge Detection,” in Proc. Int. Roy. Soc. London, Vol.B275, pp.187-217, 1980.
[39] John Canny, “Finding Edges and Lines in Images, ” MIT Artif. Intell. Lab., Cambridge, MA, Tech. Rep. AI-TR-720, 1983.
[40] I.E. Abdou and W.K. Pratt, “Quantitative Design and Evalution of Enhancement and Thresholding Edge Detectors,”in Proc. Int. IEEE, Vol.67, no.5, pp. 753-763, May 1979.
[41] W.K.Pratt, Digital Image Processing, New York: Wiley-Interscience, 1978.