本論文旨在發展整合人臉偵測技術及識別方法，於整合人臉偵測法中結合目前現存的兩大方式，分別為色彩與紋理特徵。本研究的動機為針對居家照護系統中，建立此系統機制，以同時達到高識別率與即時偵測能力。目前主要有兩大獨立發展之人臉偵測技術，分別為色彩基礎法與紋理特徵法。對於以色彩為主之方式，其特色為可用於偵測人體膚色，並進一步做膚色定位藉以找出人臉中嘴唇及眼睛色彩，但其偵測率通常是不令人滿意。於紋理特徵人臉偵測法中，首先將影像轉換到灰階色彩空間，活用此法於影像上搜尋人臉位置，但其盲目搜尋方式較難達成即時應用之需求。於本文中將結合上述兩技術之特性整合成-色彩與特徵之人臉偵測法。本文活用膚色偵測法尋找影像中人體膚色所在位置，以減少後續紋理特徵法之搜尋範圍，免除盲目搜尋問題，完成人臉偵測程序之應用。為了改善人臉偵測率於文中加入可針對特定居家對象建立之高斯膚色模型，藉以達成彩色膚色分割與定位，而後與紋理特徵法結合並使用Haar like特徵與Adaboost演算法及串接式分類器找到影像中之人臉位置。人臉識別所用技術為費雪線性鑑別法以建模，與歐式距離來加以判別所屬人臉類別。並採取直方圖等化與小波轉換及原影像直接建模之前處理方式比較識別率與即時能力。最後在實驗結果中加入人臉偵測與識別整合實驗探討錯誤偵測框對識別率的影響。此外，本論文亦發展快速二維經驗模態分解演算法，可用於分解非線性、非穩態影像並找出人臉表情紋理特徵及還原相加成原影像之技術，未來可將其應用於人臉表情特徵萃取與識別中。

The subject in this thesis is developed a combinational technology for face detection and recognition, and this combination is based on two existed face detections, color and texture features. Concurrently achieve the higher recognition rate and real-time detection capability for the digital home-cared systems is the motivation of this research. There are two major and independent face detection technologies, color-based and texture-based. For color-based feature detection, it detects the skin color firstly, and then locates the face based on lip’s or eye’s color, but the accuracy rate of detection is often unacceptable. For texture-based feature detection, it transfers image to gray color firstly, and then locates the face based on the textural features in whole face, but the almost blind searching during the face location is difficult to achieve the requirement of real time applications. In this thesis, the skin color is firstly detected for reducing the possible area there faces included, and then blindingly searches the face features in those bounded area, face recognition process is applied in finally. In order to improve the accuracy rate during face detection, a Gaussian skin color model of family is built. During the searching of face features in color-bounded area, a combination process of Haar likes feature, Adaboost algorithm and cascade classification is applied to locate the position of face. In the face recognition, the Fisher linear discrimination is used to build face models, and Euclidian distance discrimination is used in the face classifications basically. Histogram equalization with wavelet also used to be a preprocess method in additional to improve the accuracy rate of recognition and the ability of real-time. Finally, a real-time bi-dimensional empirical mode decomposition is developed to decompose a photo into some pictures with face texture in different resolution, this achievement is the foundation of the future research about the recognition of facial expression.

第一章緒論
第二章人臉偵測
第三章人臉辨識
第四章 二維經驗模態分解
第五章 實驗結果與討論
第六章 結論與展望
參考文獻
附錄A小波轉換
附錄B希爾伯特黃轉換

1. 井上誠喜、八木伸行，C語言數位影像處理，全華科技圖書股份有限公司，民國九十四年。
2. 何智光，「3D人臉動畫模擬」，義守大學電機工程學系研究所，碩士論文，民國九十四年。
3. 林宸生，數位信號-影像與語音處理，全華科技圖書股份有限公司，民國九十二年。
4. 周哲帆、劉昆峰，「門禁監視之影像處理技術」，清雲科技大學電子工程系，大學專題製作報告，民國九十四年。
5. 周鵬程，類神經網路入門-活用MATLAB，全華科技圖書股份有限公司，民國九十一年。
6. 張智星，MATLAB程式設計與應用，初版，清蔚科技，新竹市，民國八十九年。
7. 陳嘉雄，「多角度人臉之二階段辨識」，國立東華大學資訊工程研究所，碩士論文，民國九十一年。
8. 黃泰祥，「具備人臉追蹤與辨識功能的一個智慧型數位監視系統」，中原大學電子工程研究所，碩士論文，民國九十二年。
9. 劉于碩，「應用經驗模態分解技術於情緒化自發語音之辨識」，清雲科技大學電子工程研究所，碩士論文，民國九十六年。
10. 劉瑞禎、于仕琪，OpenCV教程-基礎篇，北京航空航天大學出版社，民國九十六年。
11. 繆紹綱，數位影像處理，普林斯頓國際有限公司，民國九十二年。
12. 謝耀璋，「主動式攝影機即時人臉追蹤之研究」，國立中山大學機械與機電工程學系，碩士論文，民國九十四年。
13. 蘇木春、張孝德，機器學習：類神經網路、模糊系統以及基因演算法則，全華科技圖書股份有限公司，民國八十六年。
14. 蘇芳生，「人臉表情辨識系統」，國立中正大學通訊工程研究所，碩士論文，民國九十三年。
15. Stan Birchfield, “Elliptical Head Tracking Using Intensity Gradients and Color Histograms”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,Santa Barbara, California, pp. 232-237, June 1998,http://vision.stanford.edu/~birch/publications/headtracker_cvpr1998.pdf.
16. Stan Birchfield, “An Elliptical Head Tracker”, In Proceedings of the 31st Asilomar Conference on Signals, Systems, and Computers,Pacific Grove, California, pp. 1710-1714, November 1997, http://www.cs.duke.edu/~tomasi/papers/birchfield/birchfieldAsilomar97.pdf.
17. Gary Bradski, Adrian Kaehler, and Vadim Pisarevsky, “Learning-Based Computer Vision with Intel’s Open Source Computer Vision Library”, Intel Technology Journal, Vol. 09, no 02, 2005, ISBN 1535-864X, http://developer.intel.com/technology/itj/2005/volume09issue02/art03_learning_vision/p01_abstract.htm.
18. Christophe Damerva,Sylvain Meignen, and Valeriee Perrier, “A Fast Algorithm for Bidimensional EMD”, IEEE Signal Processing Letters, Vol. 12 ,No. 10, pp.701-704, October 2005.
19. Richard O. Duda, Peter E.Hart, and David G. Stork, Pattern Classification, WILEY, Second Edition, 2001.
20. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Digital Image Processing Using MATLAB, Princeton, 2004.
21. Huang, Norden E and Shen, Samuel S, Hilbert-Huang Transform And Its Applications, World Scientific Pub Co Inc, 2005.
22. P. Kakumanu, S. Makrogiannis, and N. Bourbakis, “A survey of skin-color modeling and detection methods”, ScienceDirect Pattern Recognition ,Vol 40, no. 03, pp. 1106-1122 , March 2007.
23. Rainer Lienhart, Alexander Kuranov, and Vadim Pisarevsky, “Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection”, Intel Corporation, Santa Clara, CA 95052,USA MRL Technical Report, December 2002,http://fsa.ia.ac.cn/files/MRL-TR-May02-revised-Dec02.pdf.
24. J.C. Nunes, et al., “Image analysis by bidimensional empirical mode decomposition”, Image and Vision Computing, Vol. 21(2003), pp.1019-1026, May 2003.
25. J.C. Nunes, S. Guyot, E. Delechelle, ”Texture analysis based on local analysis of the Bidimensional Empirical Mode Decomposition” Machine Vision and Applications , Springer Berlin / Heidelberg ,vol 16, no 3, 25 February 2005.
26. Michael Oren et al., ”Pedestrian Detection Using Wavelet Templates”, In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp.193-199,1997 , http://ieeexplore.ieee.org/iel3/4821/13322/00609319.pdf.
27. Constantine P. Papageorgiou, Michael Oren,and Tomaso Poggio, “A General Framework for Object Detection”, In Proceedings of the International Conference on Computer Vision, pp.555-562,1998 ,http://ieeexplore.ieee.org/iel4/5756/15374/00710772.pdf.
28. David G. Stork and Elad Yom-Tov, Computer Manual in MATLAB to accompany Pattern Classification, WILEY, Second Edition, 2004.
29. Kinh Tieu and Paul Viola., ”Boosting Image Retrieval”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2000,http://people.csail.mit.edu/tieu/papers/cvpr00.pdf.
30. Paul Viola and Michael Jones., “Rapid Object Detection using a Boosted Cascade of Simple Features”., In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR),2001, http://research.microsoft.com/~viola/Pubs/Detect/violaJones_CV.PR2001.pdf.
31. Paul Viola and Michael Jones., “Robust Real-time Object Detection”, Cambridge Research Laboratory Technical Report Series, February 2001, http://www.hpl.hp.com/techreports/Compaq-DEC/CRL-2001-1.pdf.
32. Adrew Webb, Statistical Pattern Recognition, WILEY, Second Edition, 2004.