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Face Tracking By Support Vector-trained Fuzzy Classifier With Focus Color And Shape Feature
|關鍵字:||face;人臉;color;shape;haar wavelet;fuzzy neural network;色彩;形狀;小波轉換;模糊類神經網路||出版社:||電機工程學系所||引用:|| E. Hjelmas and B.K. Low, “Face Detection: A Survey,” Computer Vision and Images Understanding, vol. 83, pp. 236-274, 2001.  M.Yang, D. Kriegman, and N. Ahuja, “Detecting Faces in Images: A Survey,” Proc. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan. 2002.  K.-K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.  H. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection,” IEEE Trans. Pattern Analysis and Machine intelligence, vol. 20, no. 1, pp. 23-38, Jan. 1998.  E. Osuna, R. Freund and F. Girosi, “Training support vector machines: an application to face detection,” Prof. IEEE Int. Conf. Computer Vision and Pattern Recognition, pp. 130-136, 1997.  R. Feraund, O.J. Bernier, J.E. Viallet, and M. 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SICE-ICASE (Society of Instrumentation and Control Engineers - Institute of Control,Automation and Systems Engineers), pp. 2985-2988, Oct. 2006  C. Lerdsudwichai, and M. Abdel-Mottaleb, “Algorithm for multiple faces tracking,” Proc. Int. Conf. Pattern Recognition, Vol. 3, pp.977-980, Aug. 2004.  R.E. Kalman: A new approach to linear filtering and prediction problems. Trans. ASME, J. Basic Eng. 82, 35 (1960).  G. Bradski, A. Kaehler, V. Pisarevsky, Learning-based computer vision with Intel's open source computer vision library, Intel Technol. J. 9(2) (2005) 119-130  CIT face database http://www.vision.caltech.edu/Image_Datasets/faces/||摘要:||
此篇論文介紹一種藉由色彩與形狀特徵值之模糊分類器所進行的人臉偵測與追蹤。而此處的分類器則是使用結合了 Takagi-Sugeno 的fuzzy if-then rules , self-splitting k-means 自我分群法與支持向量器 (support vector learning) 所賦予的高等綜合能力的模糊系統：Fuzzy Classifier With Self-Splitting K-menas And Support Vector Learning (FC-SSKSV)。在臉部偵測的整個流程中，首先我們利用 FC-SSKSV 分類器把色彩空間裡的膚色判別出來。而為了去除一些非膚色範圍的雜訊與補強膚色範圍的完整性，我們利用了型態學的“opening”運算及相鄰膚色的補強來進行處理。接下來針對膚色分割出來的人臉候選區找出它的最佳近似橢圓形。然後對人臉候選區進行以 YCbCr 色彩空間為基礎的 Haar 小波轉換，之後可以根據 Haar 小波轉換在人臉候選區裡的表現來定位出眼睛與嘴巴的位置。眼睛、嘴巴與人臉的色彩特徵可以很直接的被提取出來。這些色彩資訊特徵接著與人臉候選區的形態特徵一起被丟入 FC-SSKSV 做最後的人臉判斷。而上述的方法我們利用 pan-tilt-zoom攝影機來實現在即時人臉追蹤系統上。跟其他的分類器及人臉偵測方法比較， FC-SSKSV 及基於其架構所實現人臉偵測的成果是更為進步的。
This thesis proposes a new face detection method by a fuzzy classifier with color and shape features. A new fuzzy classifier with Self-Splitting K-means and Support Vector learning (FC-SSKSV) is proposed. The FC-SSKSV consists of Takagi-Sugeno type fuzzy if-then rules. The self-splitting k-means clustering algorithm determines the number of rules and rule antecedent part parameters. A linear support vector machine determines the rule consequent part parameters to endow the FC-SSKSV high generalization ability. In the proposed face detection method, the FC-SSKSV is first applied to segment human skin pixels in color space. Morphological opening and neighborhood averaging operations are employed on segmented pixels to eliminate noise and generate face candidates. A best fitting ellipse of each candidate is found to obtain its shape features. The Haar wavelet transformation is applied to the candidates in YCbCr color space, and selected wavelet representations are used to locate the eyes and mouth. Color features of located eyes, mouth, and face skin are extracted. These focus color features, together with shape features, serve as inputs to another FC-SSKSV for final face detection. The proposed face detection method is employed in a real-time face tracking system with a pan-tilt-zoom camera. Performance of the FC-SSKSV and face detection method is compared with other classifiers and detection methods, respectively, to demonstrate their advantages.
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