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Author: 施宏政
Thesis Title: 基於模糊推論之膚色補償方法應用於彩色影像
Advisor: 葉榮木
Degree: 碩士
Master
Department: 機電工程學系
Department of Mechatronic Engineering
Thesis Publication Year: 2004
Academic Year: 92
Language: 中文
Number of pages: 67
Keywords (in Chinese): 光線補償模糊邏輯色彩空間膚色相似度模型唇色檢測
Keywords (in English): light compensation, fuzzy logic, color space, skin color similar model, lip detector
Thesis Type: Academic thesis/ dissertation
Reference times: Clicks: 317Downloads: 24
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  • 彩色影像的色彩資訊應用在物體的檢測(例如利用膚色做人臉檢測之
    前置處理)是相當有用的訊息,但是應用色彩作為物體檢測的特徵,首先
    必須面對的是光線所造成的影響。在過亮、過暗、陰影或偏光的環境下皆
    會使物體檢測的檢測率大為降低。
    在本文中採用分散式模糊邏輯推論,利用影像之平均亮度與像素之
    RGB值,建構分散式模糊推論引擎,分別推論出像素RGB之補償量,而得
    到一種適應不同狀況(光線偏暗或偏亮)之光線補償演算法,解決人臉影像
    在不均勻光源的影響,並且配合利用模糊關係與模糊統計試驗法所建構之
    膚色相似模型,設計人的膚色檢測器,找出人臉膚色的區域。
    並且利用嘴唇的顏色特徵,設計一個唇色檢測器與眼球其低灰度值的
    特徵,設計眼睛檢測器,配合人臉五官的幾何關係找出人臉的位置,此方
    法可減少複雜背景的影響且可解決多人臉重疊在一個區域的情況。
    經由實驗得知此模糊推論模式能有效解決彩色影像之光線補償的問
    題,且本文所提之演算法亦可處理複雜背景的影像,因而可提高人臉之檢
    測率。

    Different environment illumination has a great impact on object detection
    and recognition. The automatic radiation correction of a highlighed object area
    or lowlighed object area plays an important role in the field of image
    processing and computer vision.
    Skin color can be used for human face detection. In the paper, we propose
    a light compensation method for skin color segmentation under varying light
    conditions. A major problem of using skin color is that a face region may not be
    detected under poor or lighting conditions. We adopted a fuzzy logic technique
    to determine the compensation value the brightness. The exposure control
    system proposed in the paper uses “RGB” and Y of pixels in the color image
    determines a compensation amount by the fuzzy reasoning.
    Based on a light compensation technique and skin color similar model
    (SSM), skin regions can be detected and then face candidates can be obtained.
    The characteristics of eyes and lips of faces are used to help to detect each face
    candidates.
    The performance of the system is evaluated through assessment
    experiments. Experimental results show that this proposed methodcan improve
    the performance of face segmentation under poor or strong light conditions and
    detect faces with wide variations in size, scale, color, position and expression in
    images.

    目錄 摘要..........................................................................................................Ⅰ Abstract.....................................................................................................Ⅱ 目錄..........................................................................................................Ⅲ 圖目錄......................................................................................................Ⅴ 表目錄......................................................................................................Ⅶ 第一章 緒論..............................................................................................1 1-1研究背景與動機.............................................................................1 1-2研究目的.........................................................................................3 1-3研究目標.........................................................................................4 1-4系統架構.........................................................................................5 1-5研究架構.........................................................................................9 1-6關鍵字...........................................................................................11 1-7論文架構.......................................................................................12 第二章 文獻探討....................................................................................13 2-1人臉檢測的問題...........................................................................13 2-2檢測方法的綜述...........................................................................14 2-3色彩空間的選擇...........................................................................18 2-4膚色模型.......................................................................................25 2-5光線補償......................................................................................27 第三章 膚色補償與膚色相似模型……................................................29 3-1膚色模型的光線補償...................................................................29 3-1-1分散式模糊推論系統...........................................................30 3-1-2模糊邏輯理論………...........................................................31 3-1-3光線補償之模糊規則...........................................................35 3-2建立膚色相似模型.......................................................................42 3-2-1模糊關係之定義與性質.......................................................42 3-2-2建立膚色相似性模型….......................................................43 3-2-3檢測膚色之準則……….......................................................44 第四章 膚色區域分割與嘴唇和眼睛之定位........................................47 4-1雜訊處理.......................................................................................48 4-2連通處理...................................................................................49 4-3嘴唇定位檢測...............................................................................51 4-4眼睛定位檢測...............................................................................54 第五章 實驗與分析................................................................................55 第六章 結論............................................................................................61 參考文獻..................................................................................................62 圖目錄 圖1.1 膚色檢測流程圖.............................................................................5 圖1.2 人臉檢測流程圖.............................................................................7 圖1.3 研究步驟流程圖...........................................................................10 圖2.1 RGB色彩立方體圖.......................................................................19 圖2.2 Additive RGB色彩系統................................................................19 圖2.3 RGB三原色分佈...........................................................................19 圖2.4 析色圖…………….......................................................................21 圖2.5 HSI 色彩空間分佈.......................................................................22 圖2.6 YCbCr之分量分析........................................................................24 圖3.1 膚色補償與膚色檢測流程圖.......................................................39 圖3.2 光線補償流程圖...........................................................................31 圖3.3 模糊邏輯推論系統之基本架構圖……………….......................32 圖3.4 min-min-max模糊推論法示意圖.................................................35 圖3.5 輸入與輸出之歸屬函數...............................................................37 圖3.6 衰減因子推論系統之輸出入之歸屬函數…………...................38 圖3.7 補償檢測效果...............................................................................41 圖3.8 膚色相似度模型...........................................................................45 圖3.9 輸入原圖.......................................................................................45 圖3.10 膚色檢測結果.............................................................................45 圖3.11 膚色檢測結果.............................................................................44 圖4.1 膚色區域分割演算法流程圖.......................................................47 圖4.2 3x3遮罩.........................................................................................48 圖4.3 框出膚色區域...............................................................................49 圖4.4 人臉五官比率...............................................................................51 圖4.5 適應性嘴唇切割演算法...............................................................52 圖4.6 嘴唇檢測結果...............................................................................53 圖4.7 嘴唇與眼睛檢測結果...................................................................54 圖5.1 實驗結果示意圖...........................................................................56 表目錄 表1.1 人臉檢測之問題分類...................................................................13 表1.2 人臉的特徵與處理方法...............................................................14 表3.1 Y-R模糊補償規則表.....................................................................38 表3.2 Y-G模糊補償規則表.....................................................................38 表3.3 Y-B模糊補償規則表.....................................................................39 表3.4 衰減因子推論之規則表...............................................................39 表5.1 影像中只含單人情況下之檢測結果...........................................55 表5.2 影像中內含多人情況下之檢測結果...........................................55

    參考文獻
    [1] H. Wang and S. F. Chang, “A highly efficient system for automatic face region detection in MPEG video,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 7, no. 4, pp. 615-628, Aug. 1997.
    [2] Chellappa R, Wilson C. L. and Sirohey S. “Human and machine recognition of faces: A survey.” Proceedings of the IEEE, 1995,83(5):705-740.
    [3] M.H. Yang, D. Kriegman, N. Ahuaja, “Detecting face in images: A survey”, IEEE T. PAMI, Vol.24, No.1,p.34-58,2001.
    [4] Gang Wei, Ishwar K. Sethi, “Omni-face detection for video/image content description”, ACM international conference on MM,CA,2001.
    [5] R. L. Hsu, M. A. Mottabeb, A. K. Jain, “Face detection in color images”, IEEE ICIP, Greece, Oct.2001.
    [6] W. Huang Q. Sun, C. P. Lam and J. K. Wu, “A robust approach to face and eyes detection from images with cluttered background”, ICPR, vol.1, p110-114, Aug. 1998.
    [7] C. Kotropoulos, A. Tefas and I. Pitas, “Frontal face authentication using morphological elastic graph matching”, IEEE Trans. IP, vol.9, p.555-560, Apr. 2000.
    [8] G. Z. Yang, T. S. Huang, “Human face detection in a complex background”, Pattern Recognition, vol. 27, No1, p53-63, 1994.
    [9] H. A. Rowley, S. Baluja and T. Kanade, “Neural network based face detection”, IEEE T. PAMI, v.20, p.23-38, Jan. 1998.
    [10] Ishii, H.. Fukumi, M.. Akamatsu, N. “ Face Detection Based on Skin Color Information in Visual Scenes by Neural Networks “, IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on , Vol.5, pp.557-563,1999
    [11] D.Anifantis, "A Neural Network Method For Accurate Face Detection on Arbitrary Images" , Wire Communications Laboratory , Univ . of Patras , 1999
    [12] E. Osuna, R. Freund, and F. Girosi, “Training Support Vector Machines: An Application to Face Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 130-136, 1997.
    [13] S. R. Gunn. “Support vector machines for classification and regression.” Technical Report, Image Speech and Intelligent Systems Research group, University of Southampton, 1997.
    [14] H. Schneiderman and T. Kanade, ”A statistical method for 3D object detection applied to face and cars”, IEEE CVPR, June, 2000.
    [15] Zarit, B. D., Super, B. J., and Quek, F. K. H. 1999. “Comparison of five color models in skin pixel classification”, In ICCV’99 Int’l Workshop on recognition, analysis and tracking of faces and gestures in Real-Time systems, 58-63.
    [16] Terrillon, J. C., Shirazi, M. N., Fukamachi, H., and Akamatsu, S. 2000. “Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images”. In Proc. of the International Conference on Face and Gesture Recognition, 54-61.
    [17] Brand, J., and Mason, J. 2000. “A comparative assessment of three approaches to pixellevel human skin-detection”. In Proc. of the International Conference on Pattern Recognition, vol. 1,1056-1059.
    [18] BROWN, D., CRAW, I., AND LEWTHWAITE, J. 2001. “A som based approach to skin detection with application in real time systems,” in Proc. of the British Machine Vision Conference, 2001.
    [19] C. Kotropoulos and I. Pitas, “Rule-Based Face Detection in Frontal Views,” Proc. Int’l Conf. Acoustics, Speech and Signal Processing, vol. 4, pp. 2537-2540, 1997.
    [20]Yanjiang Wang and Baozong Yuan, “A novel approach for human face detection from color images under complex background,” Pattern Recognition 34(10): 1983-1992 (2001)
    [21] G. Yang and T. S. Huang, “Human Face Detection in Complex Background,” Pattern Recognition, vol. 27, no. 1, pp. 53-63, 1994.
    [22] G. Yang and T. S. Huang, “Human Face Detection in Complex Background”, Pattern Recognition, vol. 27, no.1, pp. 53-63, 1996.
    [23] CHAI, D., AND BOUZERDOUM, A. 2000. “A bayesian approach to skincolor classification in ycbcr color space,” in Proceedings IEEE Region Ten Conference (TENCON’2000), vol. 2, 421–424.
    [24] M. J. Jones and J. R. Rehg, “Statistical color models with application to skin detection,” Tech. Rep. CRL 98/11, Compaq Cambridge Research Lab., 1998.
    [25] Otsu, N.,“A threshold selection method from gray-level histogram”, IEEE Trans. System, Man, and Cybernetics, Vol. 9, No. 1, pp.62-66 (1979).
    [26] Zhang, L. and Lenders, P. “Knowledge –based eye detection for human face recognition”, Proceedings of Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, August 2000, Brighton, UK, pp. 117-120 (2000).
    [25] I. Craw, D. Tock, and A. Bennett, “Finding Face Features,” Proc. Second European Conf. Computer Vision, pp. 92-96, 1992.
    [26] C. Kotropoulos and I. Pitas, “Rule-Based Face Detection in Frontal Views,” Proc. Int’l Conf. Acoustics, Speech and Signal Processing, vol. 4, pp. 2537-2540, 1997.
    [27] R. Kjeldsen and J. Kender, “Finding Skin in Color Images,” Proc. Second Int’l Conf. Automatic Face and Gesture Recognition, pp. 312-317, 1996.
    [28] A. Lanitis, C.J. Taylor, and T.F. Cootes, “An Automatic Face Identification System Using Flexible Appearance Models,” Image and Vision Computing, vol. 13, no. 5, pp. 393-401, 1995.
    [29] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
    [30]. Micheal Oren,Constantine Papageorgiou,Pawan Sinha,Edgar Osuna,Tomaso Poggio,”Pedestrian Detection Using Wavelet Templates”CVPR 97,June 17-19,Puerto Rico.
    [31]. C. Garcia,G. Zikos,G. Tziritas,”Wavelet packet analysis for face recognition”,Image and Vision computing 18(2000)289-297.
    [32] S.A. Sirohey, “Human Face Segmentation and Identification,” Technical Report CS-TR-3176, Univ. of Maryland, 1993
    [33] H.P. Graf, T. Chen, E. Petajan, and E. Cosatto, “Locating Faces and Facial Parts,” Proc. First Int’l Workshop Automatic Face and Gesture Recognition, pp. 41-46, 1995.
    [34] T. Sakai, M. Nagao, and S. Fujibayashi, “Line Extraction and Pattern Detection in a Photograph,” Pattern Recognition, vol. 1, pp. 233-248, 1969.
    [35] I. Craw, H. Ellis, and J. Lishman, “Automatic Extraction of Face Features,” Pattern Recognition Letters, vol. 5, pp. 183-187, 1987.
    [36] Vezhnevets V., Sazonov V., Andreeva A., "A Survey on Pixel-Based Skin Color Detection Techniques". Proc. Graphicon-2003, pp. 85-92, Moscow, Russia, September 2003.
    [37] PEER, P., KOVAC, J., AND SOLINA, F. 2003. Human skin colour clustering for face detection. In submitted to EUROCON 2003 – International Conference on Computer as a Tool.
    [38]AHLBERG, J. 1999. A system for face localization and facial feature extraction. Tech. Rep. LiTH-ISY-R-2172, Linkoping University.
    [39] HSU, R.-L., ABDEL-MOTTALEB, M., AND JAIN, A. K. 2002. “Face detection in color images,” IEEE Trans. Pattern Analysis and Machine Intelligence 24, 5, 696–706.
    [40] GOMEZ, G., AND MORALES, E. 2002. “Automatic feature construction and a simple rule induction algorithm for skin detection,” in Proc. of the ICML Workshop on Machine Learning in Computer Vision, 31–38.
    [41] SABER, E., AND TEKALP, A. 1998. Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions. Pattern Recognition Letters 19, 8, 669–680.
    [42] YANG, M., AND AHUJA, N. 1999. Gaussian mixture model for human skin color and its application in image and video databases. In Proc. of the SPIE: Conf. on Storage and Retrieval for Image and Video Databases (SPIE 99), vol. 3656, 458–466.
    [43] Zong-Mu Yeh, “Adaptive multivariable fuzzy logic controller,” Fuzzy Sets and Systems, Vol.86, pp.43-60, 1997.
    [44] Z. M. Yeh, “A Systematic Method for Design of Multivariable Fuzzy Logic Control Systems,” IEEE Transactions on Fuzzy System, Vol.4, No.3, pp.215-228, 1998.
    [45] K. Wogsritong, K. Kittayaruasiriwat, F. Cheevasuvit, K. Dejhan, and A. Somboonkaew, “Contrast enhancement using multipeak histogram equalization with brightness preserving”, IEEE Conference on Circuits and Systems,Bangkok, Thailand, pp. 455-458, 1998.
    [46] Rein-Lien Hsu, Mohamed Abdel-Mottaleb, and Anil K. Jain, “Face Detection in Color Image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696-706, May 2002.
    [47] Hayit Greenspan, Jacob Goldberger, Itay Eshet, “Mixture model for face-color modeling and segmentation,” Pattern Recognition Letters 22(14): 1525-1536 (2001)
    [48] S. Shimizu, T. Kondo, T. Kohashi, M. Tsuruta, and T. Komuro. “A new algorithm for exposure control based on fuzzy logic for video cameras,” IEEE Transations on Consumer Electronics, 38(3):617–623, Aug 1992.
    [49] M. Murakami and N. Honda. “An exposure control system of videocameras based on fuzzy logic using color information,” Fuzzy Systems, Proceedings of the Fifth IEEE International Conference, 3:2181–2187, 1996.
    [50] 阮亨中、吳柏林(2000),「模糊數學與統計應用」,俊傑書局股份有限公司。
    [51] 藎爐(1991),「實用模糊數學」,亞東書局。
    [52] D. Chai and K. N. Ngan, “Face segmentation using skin color map in videophone applications," IEEE Transactions on Circuits and Systems for Video Technology, vol. 9, no. 4, pp. 551-564, Jun. 1999.
    [53]. Saad A.sirohey,Azriel Rosenfeld,”Eye detection in a face image using linear and nonlinear filters”,Pattern Recognition 34(2001) p1367-1391.
    [54] Yahoo News Photos, http://dailynews.yahoo.com, http://dailynews.yahoo.com.tw
    [55] http://www.stanford.edu/class/ee368/Proj

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