Please use this identifier to cite or link to this item:
標題: 藉由色彩與形狀特徵值之支持向量模糊分類器的人臉追蹤
Face Tracking By Support Vector-trained Fuzzy Classifier With Focus Color And Shape Feature
作者: 管振寧
Guan, Chen-Ning
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: [1] E. Hjelmas and B.K. Low, “Face Detection: A Survey,” Computer Vision and Images Understanding, vol. 83, pp. 236-274, 2001. [2] 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. [3] 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. [4] 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. [5] 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. [6] R. Feraund, O.J. Bernier, J.E. Viallet, and M. Collobert, “A fast and accurate face detector based on neural networks,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 1, pp. 42-53, Jan. 2001. [7] Z. Suna , G. Bebisa, and R. Millerb, “Object detection using feature subset selection,” Pattern Recognition, vol. 37, no. 11, pp. 2165- 2176, 2004. [8] C. Papageorgiou and T. Poggio, “A trainable system for object detection,” Int. J. Computer Vision, vol. 38, no. 1, pp. 15-33, 2000. [9] R. Lienhart and J. Maydt, “An extended set of Haar-like features for rapid object detection,” Proc. of IEEE Int. Conf. Image Processing, pp. 900-903, 2002. [10] P. Viola and M. M. Jones, “Robust real-time face detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004. [11] P. Shih and C. Liu, “Face detection using discriminating feature analysis and support vector machine,” Pattern Recognition, vol. 39, pp. 260-276, 2006. [12] D. Nguyen, D. halupka, P. Aarabi, and A. Sheikholeslami, “Real-time face detection and lip feature extraction using filed-programmable gate arrays,” IEEE Trans. Systems, Man, and Cyber., - Part B: Cybernetics, vol. 36, no. 4, pp. 902-912, 2006. [13] Y. B. Sun and J. T. Kim, and W. H. lee, “Extraction of face objects using skin color information,” Proc. IEEE Int. Conf. Communications, Circuits and Systems and West Sino Expositions, vol. 2, pp. 1136-1140, 2002. [14] R. L. Hsu, M. A. Mottaleb, and A. K. Jain, “Face detection in color images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696-706, May 2002. [15] O. Ikeda, “Segmentation of faces in video footage using HSV color for face detection and image retrieval,” Proc. IEEE Int. Conf. Image Processing, vil. 3, pp. 913-916, 2003. [16] Z. Jin, Z. Lou, J. Yang, and Q. Sun, “Face detection using template matching and skin-color information,” Neucomputing, vol. 70, pp. 794-800, 2007. [17] C. F. Juang and S. J. Shiu, “Using self-organizing fuzzy network with support vector learning for face detection in color images,” Neurocomputing, vol. 71, no. 16-18, pp. 3409-3420, Oct. 2008. [18] C. Garica and M. Delakis, “Convolutional face finder: a neural architecture of fast and robust face detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1408-1423, Nov. 2004. [19] C. F. Juang, S. H. Chiu, and S. W. Chang, “A self-organizing TS-type fuzzy network with support vector learning and its application to classification problems,” IEEE Trans. Fuzzy Systems, vol. 15, no. 5, pp. 998-1008, Oct. 2007. [20] H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” International Journal of Pattern Recognition, vol. 34, no. 12, pp. 2259-2281, September 2000. [21] M. J. Jones and J. M. Rehg, “Statistical color models with application to skin detection,” Int. Journal of Computer Vision, vol. 46, no. 1, pp. 81-96, Jan. 2002. [22] S. Mckenna, S. Gong, and Y Raja, “Modeling facial colour and identity with Gaussian mixtures,” Pattern Recognition, vol. 31, no. 12, pp. 1883-1892, 1998. [23] C. F. Juang, H. S. Permg, and S. H. Chiu, “Block histogram-based neural fuzzy approach to the segmentation of skin colors,” Journal of Information Science and Engineering, vol. 23, no. 6, pp. 1737-1752, Nov. 2007. [24] N. Cristianini and J. S.-Taylor, “An Introduction to Support Vector Machines And Other Kernel-based Learning Methods,” Cambridge University Press, 2000. [25] Stollnitz, E., DeRose, T., and Salesin,D. 1994. Wavelets for computer graphics: A primer. Technical Report 94-09-11, Department of Computer Science and Engineering, University of Washington. [26] H. C. Shin, E. G. Lim, and D. H. Hwang, “Real-Time Face Tracking for Tele-Operated Mobile Robot with an Embedded System,” International Joint Conference. SICE-ICASE (Society of Instrumentation and Control Engineers - Institute of Control,Automation and Systems Engineers), pp. 2985-2988, Oct. 2006 [27] C. Lerdsudwichai, and M. Abdel-Mottaleb, “Algorithm for multiple faces tracking,” Proc. Int. Conf. Pattern Recognition, Vol. 3, pp.977-980, Aug. 2004. [28] R.E. Kalman: A new approach to linear filtering and prediction problems. Trans. ASME, J. Basic Eng. 82, 35 (1960). [29] 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 [30] CIT face database
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.
其他識別: U0005-1908200920161000
Appears in Collections:電機工程學系所

Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.