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Design and Implementation of an Intelligent Robotic Head System
|關鍵字:||Intelligent;智慧型;Robotic head system;機器人頭顱||出版社:||電機工程學系所||引用:||References  A.Albiol, L. Torres, E.J. Delp, “Optimum color spaces for skin detection,” Politechnic University of Valencia Spain, vol.1, , pp.122-124, 2001.  M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuro-science, vol.3, no.1, pp.71-86, 1991.  S. H. Jeng, H. Y. Mark Liao, C. C. Han, M. Y. Chern, and Y. T. Liu, “An efficient approach for facial feature detection using geometrical face model,” in Proc. of the 13th International Conference on Pattern Recognition, Vienna, Austria, 1996, vol.3, pp.426-430.  K. Sobottka and I. Pitas, “Extraction of facial regions and features using color and shape information,” in Proc. 13th International Conference on Pattern Recognition, , Vienna, Austria, Aug. 1996, pp.421-425.  H. Wu, Q. Chen, and M. Yachida, “A fuzzy-theory-based face detector,” in Proc. 13th International Conference on Pattern Recognition, Vienna, Austria, Aug. 1996 pp.406-410.  C. Cortes, V. Vapnik, “Support vector networks,” Machine Learning, vol. 20, pp. 273-297, 1995.  E. Osuna, Support Vector Machines: Training and Applications, Ph.D. thesis, Dept. of EECS, Massachusetts Institute of Technology, 1998.  E. Osuna, R. Freund, and F. Girosi, “Training support vector machines: an application to face detection,” in Proc. Computer Vision and Pattern Recognition, 1997, pp.17-19.  S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back. “Face recognition: A convolutional neural network approach,” in Proc. IEEE Trans. Neural Networks, vol.8, pp.98-113, 1997.  R. Chellappa, C. L.Wilson, and S. Sirohey. “Human and machine recognition of faces: A survey,” in Proc. IEEE, vol.83, pp.705- 741, May 1995.  D. Valentin, H. Abdi., A. J. O'Toole, and G. W. Cottrell, “ Connectionist models of face processing: A survey, ”Pattern Recognition, vol.27 pp.1209-1230, 1994.  A. Samal and P. A. Iyengar, “Automatic recognition and analysis of human faces and facial expressions: A survey,” Pattern Recognition, vol.25, pp.65-77, 1992.  A. G. O. Mutambara, Decentralized estimation and control for multisensor systems, CRC Press LLC, 1998.  L. A. Zadeh, “Fuzzy logic,” Computer, vol. 21, no. 4, pp. 83-93, 1988.  G. N. Saridis, Stochastic processes, estimation, and control: the entropy approach, John Wiley & sons, New York, 1995. K. Pearson, “On line and planes of closest fit to systems of points in space,” Philosophy Magazine, vol.2, pp.559 - 572, 1901. H. Hotelling, “of a complex of statical variables into principal components,” Journal of Educational Psychology, vol.24, pp.417 - 441, 1933.  I. T. Jolliffe, Principal Component Analysis, Springer-Verlag, 1986.  V. N. Vapnik. Statistical learning theory. John Wiley & Sons, New York, 1998.  J. Friedman. Another approach to polychotomous classification. Technical report, Department of Statistics, Stanford University, 1996.  U. KreBel, “Pairwise classification and support vector machines,” in proc Advances in Kernel Methods－Support Vector Learning, pp.255-268, Cambridge, MA, 1999, MIT Press.  C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.  J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, Vol. 2, pp. 121-167,1998.  S. Haykin, Neural Networks: A Comprehensive Foundation, Second Edition, New Jersey: Prentice-Hall, 1999.  F. L. Lewis, Optimal estimation with an introduction to stochastic control theory, John Wiley & Sons, 1986.  P. S. Maybeck, Stochastic Models, Estimation, and Control, vol.1, New York, Academic Press, 1979.  J. Z. Sasiadek and Q. Wang, “Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle,” in Proceeding the 1999 IEEE Conference on Robotics and Automation, Detroit, Michigan, pp. 2970-2975, May 1999.  J. Z. Sasiadek, and Q. Wang, “Fuzzy adaptive Kalman filtering for INS/GPS data fusion and accurate positioning,” in Proc. of the 2001 IFAC International Symposium on Aerospace Control, Bologna, Italy, pp.451-459,2001.||摘要:||
本篇論文發展一個擁有臉部偵測、追蹤、辨識以及表情識別的智慧型機器人頭顱的技術。 利用FPGA及伺服馬達來建構一個智慧型的機器人頭顱之後，本論文採用一般標準的影像處理方法來偵測人臉，並利用模糊凱爾曼濾波器(FKF)來進行人臉追蹤 ， 再透過主成分分析法(PCA)以及支援向量機(SVM)來進行人臉鑑定和臉部的表情識別。利用電腦模擬以及實驗來加以驗證模糊凱爾曼濾波器(FKF)在人臉追蹤的效果，再透過機器人頭顱系統的實驗來證實臉部辨識及表情識別的效能，這些被提議的技術可能有助於工作服務型機器人領域的專業人士。
This thesis develops techniques for face detection and tracking, face identification and facial expressions recognition of an intelligent robotic head system. After constructing a physical intelligent robotic head using FPGA and RC servomotors, this thesis applies a standard image processing algorithm to detect faces of any users and then track their faces using a fuzzy Kalman filtering scheme. Face identification and facial expressions recognition are achieved by means of principal component analysis (PCA) and support vector machine (SVM). Computer simulations and experimental results are conducted to verify the effectiveness of the proposed face tracking method. The performance and merit of the proposed face identification and face expressions recognition algorithms are exemplified by performing experiments on the experimental robotic head system. The proposed techniques may be of interesting to professionals working in the field of mobile service robots coexisting with people.
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