Autor: Oscar Manuel Duque Suarez

Director: PhD. Oscar Eduardo Gualdrón Guerrero

 

RESUMEN

 

El objetivo a lograr en este trabajo era un sistema de reconocimiento de rostros mediante la hibridación de técnicas de reconocimiento de patrones, inteligencia artificial y visión artificial, enfocado a la seguridad e interacción robótica social. Las muestra de rostros usados en el diseño, prueba e implementación del sistema de reconocimiento correspondieron a bases de datos de rostros obtenidas de centros de investigación, universidades y agencias de seguridad que las aportan para ayudar al avance, desarrollo y difusión de las técnicas de reconocimiento de patrones y visión artificial. Se logró de manera exitosa la fusión de las técnicas propias del reconocimiento de patrones, VA e IA a fin de lograr la sinergia de las prestaciones de dichas disciplinas y de esta manera se obtuvo un sistema de reconocimiento facial robusto con implicaciones en sistemas de seguridad (industrial y comercial) e interacción robótica social.

Se obtuvo un alto índice de aciertos en la identificación facial y reconocimiento dado que la hibridación con técnicas de IA es promesa de un fortalecimiento del potencial de identificación y ampliación del nivel de aciertos dado que se han obtenido muy buenos indicadores de resultados en los centros de estudios donde se encuentra en estudio y evolución. El propósito que se estableció fue potenciar y mejorar los niveles de reconocimiento obtenidos con técnicas de reconocimiento de patrones y de visión artificial. También la presente investigación desarrollo algoritmos para el reconocimiento de género y reconocimiento gestual, usando para ello; técnicas de modelos activos (ASM) y el método detector de Harris para establecer los patrones con los que fueron entrenados los clasificadores neuronales para cubrir con ese objetivo.

 

Bibliografía

 

[1]. Abdel-Qadar, I., Pashaie-Rad, S., Abudayeh, O., and Yehia, S.: PCA-based algorithm for unsupervised bridge crack detection. Advances in Engineering Software, 37 (12), 771–778 (2006).

 

[2]. Anderson, T.W.: An Introduction into Multivariate Statistical Analysis. John Wiley & Sons, New York, (1958).

 

[3]. Ahmed Bilal, Ashraf Simon, Lucey Jeffrey, F. Cohn, Tsuhan Chen, Zara Ambadar, Kenneth M. Prkachin Patricia E. Solomon “The Painful Face – Pain Expression Recognition Using Active Appearance Models”, 2009.

 

[4]. Amit Jain, Jeffrey Huang and Shiaofen Fang, “Gender Identification Using Frontal Facial Images”, Department of Computer and Information Sciences, 2005.

 

[5]. Anderson, J. R., D. Bothell, M. D. Byrne, S. Douglass, C. Lebiere and Y. Qin: An integrated theory of the mind. Psychological Review, 111:1036{1060, 2004.

 

[6]. Bakshi, B.R., Multiscale pca with application to multivariate statistical process monitoring. AIChE Journal, 44 (7), 1596–1610 (1998).

 

[7]. Barnett, V.: Interpreting Multivariate Data. John Wiley & Sons, New York (1981).

 

[8]. Breazeal, Cynthia: Emotion and sociable humanoid robots. International Journal of Human-Computer Studies, 59(1-2):119{155, 2003.

 

[9]. Brendan Klare and Anil K- Jain, “On a Taxonomy of Facial Features”.

 

[10]. Breuing A, Waltinger U, Wachsmuth I (2011) Harvesting Wikipedia Knowledge to Identify Topics in Ongoing Natural Language Dialog, Proceedings of the 2011 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2011). IEEE: 445–450.

 

[11]. Carolina Cebrecos del Castillo, Abel Lozano Prieto, Agustín Nieto Domínguez, Robots inteligentes autónomos de nueva generación, Universidad Carlos III de Madrid, 2011.

 

[12]. Chen, P. and Suter, D.: An analysis of LÍNEAr subspace approaches for computer vision and pattern recognition. International Journal of Computer Vision, 68 (1), 83–106 (2006).

 

[13]. Chennubhotla, C. and Jepson, A.: Sparse pca extracting multi-scale structure from data. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 1, 641–647 (2001).

 

[14]. Cho, H.W.: Nonlinear feature extraction and classification of multivariate process data in kernel feature space. Expert Systems with Applications, 32 (2), 534–542 (2007).

 

[15]. Danijela Vukadinovic and Maja Pantic., “Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers ", IEEE International Conference on Systems, Man and Cybernetics Waikoloa, Hawaii October 10-12, 2005.

 

[16]. Dong, D. and McAvoy, T.J.: Nonlinear principal component analysis–based on principal curves and neural networks. Computers & Chemical Engineering, 20 (1), 65–78 (1996)

 

[17]. Du, Q. and Chang, C.: LÍNEAr mixture analysis-based

 

compression for hyper- spectal image analysis. IEEE Transactions on Geoscience and Remote Sensing, 42 (4), 875–891 (2004)

[18]. Esbensen, K. and Geladi, P.: Strategy of multivariate image analysis (MIA). Chemometrics & Intelligent Laboratory Systems, 7 (1-2), 67–86 (1989)

 

[19]. Fisher, R. and MacKenzie, W.: Studies in crop variation, ii, the manurial re- sponse of different potato varieties. Journal of Agricultural Science, 13, 411–444 (1923)

 

[20]. Ginés García Mateos, Cristina Vicente Chicote, " A New Model and Process Architecture for Facial Expression Recognition", Dpto. de Lenguajes y Sistemas Informáticos, Universidad de Murcia, 2000.

 

[21]. Golub, G.H. and van Loan, C.F.: Matrix Computation. John Hopkins, Balti- more, (1996).

 

[22]. Gregory Shakhnarovich y Baback Moghaddam, “Handbook of Face Recognition”, Spriger-Verlag, Cap 7.

 

[23]. Gustavo A. Betancourt, “Las máquinas de soporte vectorial (SVMs)”, Scientia et Technica Año XI, No 27, Abril 2005. UTP. ISSN 0122-1701.

 

[24]. Hastie, T. and Stuetzle, W.: Principal curves. Journal of the American Statis- tical Association 84 (406), 502–516 (1989).

 

[25]. Hertz, J., Krogh, A., and Palmer, R.G.: Introduction to the Theory of Neural Computing. Addison-Wesley, Redwood City, CA (1991).

 

[26]. Hotelling, H.: Analysis of a complex of statistical variables into principal com- ponents. Journal of Educational Psychology, 24 417–441 (1933).

 

[27]. Hu chuan Lu,Hui Lin, “ Gender Recognition using Adaboosted Feature”, Dalian University of Technology Dailan.

 

[28]. Aliaa A. A. Youssif, Wesam A. A. Asker, “Automatic Facial Expression Recognition System Based on Geometric and Appearance Features”, Canadian Center of Science and Education, 2011

 

[29]. Imola K. Fodor, “A survey of dimension reduction techniques”, Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, June 2002.

 

[30]. Bouchra Abboud, Franck Davoine, “Facial expression recognition and synthesis based on an appearance model”, Elsevier, 2004

 

[31]. Jackson, J.E.: Principal components and factor analysis: Part III: What is factor analysis. Journal of Quality Technology, 13 (2), 125–130 (1981).

 

[32]. Jackson, J.E.: A Users Guide to Principal Components. Wiley Series in Prob- ability and Mathematical Statistics, John Wiley & Sons, New York (1991).

 

[33]. Huchuan Lu & Yingjie Huang, Automatic gender recognition based on pixel-pattern-based texture feature, Journal of real time image processing, Springer, Japan, Abril, 2012.

 

[34]. Jia, F., Martin, E.B., and Morris, A.J.: Non-LÍNEAr principal component analysis for process fault detection. Computers & Chemical Engineering, 22 (Supple- ment), S851–S854 (1998).

 

[35]. Jill Fain Lehman, John Laird and Paul Rosenbloom: A gentle introduction to Soar, an architecture for human cognition. PhD thesis, University of Michigan EECS Department, Michigan, 2006.

 

[36]. Joliffe, I.T.: Principal Component Analysis. Springer, New York, (1986).

 

[37]. Juan Carlos Moctezuma, “Neural Network Toolbox de Matlab”, Ciencias Computacionales- INAOE, 2006.

 

[38]. Kazuhiko Kawamura, Stephen M. Gordon, Palis Ratanaswasd, Erdem Erdemir and Joseph F. Hall ; Implementation of cognitive control for a humanoid robot;center for intelligent systems, vanderbilt university, VU Box 350131 B, Nashville TN 37235-0131, USA, 2008.

 

[39]. Kim, K.I., Jung, K., and Kim, H. J.: Face recognition using kernel principal component analysis. IEEE Signal Processing Letters, 9 (2), 40–42 (2002)

 

[40]. Kramer, M.A.: Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 37 (3), 233–243 (1991).

 

[41]. Kruger, U., Antory, D., Hahn, J., Irwin, G.W., and McCullough, G.: Introduc- tion of a nonlinearity measure for principal component models. Computers & Chemical Engineering, 29 (11-12), 2355–2362 (2005).

 

[42]. Krzanowski, W. J.: Cross-validatory choice in principal component analysis: Some sampling results. Journal of Statistical Computation and Simulation, 18, 299–314 (1983)

 

[43]. Kwok, J.T.Y. and Tsang, I.W.H.: The pre-image problem in kernel methods. IEEE Transactions on Neural Networks, 15 (6), 1517–1525 (2004)

 

[44]. Kyungnam Kim, Face Recognition using Principle Component Analysis, Department of Computer Science University of Maryland, USA, 1997.

 

[45]. Leeuw J. d.: Nonlinear principal component analysis. In: Caussinus, H., Et- tinger, P., and Tomassone, R. (eds) Proceedings in Computational Statistics (COMPSTAT 1982) October 30 – September 3, Toulouse, France 1982. Physica- Verlag, Wien (1982).

 

[46]. Lees A, Vanrenterghem J, Barton G, Lake M.” Detector of landmark points on human face” Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University, 2010.

 

[47]. Li Huang and Congyong Su., "Bayesian Network Enhanced Prediction for Multiple Facial Feature Tracking" Electronic Letters on Computer Vision and Image Analysis 5(3):157-169, 2005.

 

[48]. Malinowski, E.R.: Factor Analysis in Chemistry. John Wiley & Sons, New York (2002).

 

[49]. Mardia, K.V., Kent, J.T., and Bibby, J.M.: Multivariate Analysis. Probability and Mathematical Statistics. Academic Press, London, (1979).

 

[50]. Marian Stewart Bartlett, Member, IEEE, Javier R. Movellan, Member, IEEE, and Terrence J. Sejnowski, Fellow, IEEE. " Face Recognition by Independent Component Analysis", IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 6, NOVEMBER 2002.

 

[51]. Mark A. Kramer, “ Nonlinear Priicipal Component Analysis Using Autoassociative Neural Networks”, Laboratory for Intelligent Systems in Procces Engineering, Massachusetts Institute of Technology, Cambridge.

 

[52]. Matthias Scholz, “Approaches to analysis and interpret biological profile data”, Arbeitsgruppe Bioinformatik, Januar 2006.

 

[53]. Mehmet Üzümcü, Alejandro F. Frangi, Johan H.C. Reiber, Boudewijn P.F. Lelieveldt, "Independent Component Analysis in Statistical Shape Models", University Medical Center, Leiden, The Netherlands, 2003.

 

[54]. Michael Lyons, Shigeru Akamatsu, Miyuki Kamachi and Jiro Gyoba., " Coding Facial Expressions with Gabor Wavelets", Third IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16 1998, Nara Japan, IEEE Computer Society, pp. 200-205.

 

[55]. Michel Valstar, Brais Martinez, Xavier Binefa, Maja Pantic, “Facial Point Detection using Boosted Regression and Graph Models”. 2011.

 

[56]. Mohammadreza Ramezanpour, Mohamad Ali Azimi, Mohammad Rahmati, “A New Method for Eye Detection in Color Images”, Science & Research Branch, 2010.

 

[57]. Morse Antony, Degreff joachim, Belpaeme Tony; Epigenetic Robotics Architecture (ERA), IEEE Transactions on Autonomous Mental Development, TAMD-2010-0035.R1, University of Plymouth, Centre for Robotics and Neural Systems.

 

[58]. Nara, Y., Jianming Y., and Suematsu, Y.: Face recognition using improved principal component analysis. In: Proceedings of 2003 International Symposium on Micromechatronics and Human Science (IEEE Cat. No.03TH8717), 77–82 (2003).

 

[59]. Nomikos, P. and MacGregor, J.F.: Monitoring of batch processes using multiway principal component analysis. AIChE Journal, 40 (8), 1361–1375 (1994)

 

[60]. Palús, M. and Dvoˇr´ak, I.: Singular-value decomposition in attractor recon- struction: Pitfals and precautions. Physica D: Nonlinear Phenomena, 5 (1-2), 221–234 (1992).

 

[61]. Parsopoulos, K.E. and Vrahatis, M. N.: Recent approaches to global optimiza- tion problems through particle swarm optimization. Natural Computing, 1 (2-3), 235–306 (2002).

 

[62]. Patrick Lucey, Member, IEEE, Jeffrey F. Cohn, Associate Member, IEEE, Iain Matthews, Member, IEEE, Simon Lucey, Member, IEEE, Sridha Sridharan, Senior Member, IEEE, Jessica Howlett, Student Member, IEEE, and Kenneth M. Prkachin, “Automatically Detecting Pain in Video Through Facial Action Units”, JOURNAL OF IEEE, 2010.

 

[63]. Pearson, C.: On lines and planes of closest fit to systems of points in space.Phil. Mag. Series B., 2 (11), 559–572 (1901).

 

[64]. Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester and Deva Ramanan. “Object Detection with Discriminatively Trained Part Based Models”. supported by the National Science Foundation under Grant, 2007.

 

[65]. Qin, S.J., Valle, S., and Piovoso, M. J.: On unifying multi-block analysis with application to decentralized process monitoring. Journal of Chemometrics, 10, 715–742 (2001).

 

[66]. Quan-xue Gao, Lei Zhang, David Zhang. “Face recognition using FLDA with single training image per person”. Applied Mathematics and Computation 205 (2008) 726–734.

 

[67]. Sch¨olkopf, B. and Smola, A. J.: Learning with Kernels: Support Vector Ma- chines, Regularization, Optimization, and Beyond. The MIT Press, Cambridge, MA (2002)

 

[68]. Sch¨olkopf, B. and Smola, A. J., and Mu¨ller, K.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10 (5), 1299–1319 (1998).

 

[69]. Serge Belongie, Member, IEEE, Jitendra Malik, Member, IEEE, and Jan Puzicha., “Shape Matching and Object Recognition Using Shape Contexts", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 24, NO. 24, APRIL 2002.

 

[70]. Shanmugam, R. and Johnson, C.: At a crossroad of data envelopment and principal component analyses. Omega, 35 (4), 351–364 (2007).

 

[71]. Srinivas, M. and Patnaik, L.M.: Genetic algorithms: A survey. Computer, 27 (6), 17–26 (1994).

 

[72]. Stefan Profanter, Cognitive architectures, Technische Universität München, Wintersemester 2012/13.

 

[73]. Stefano Berretti, Alberto del Bimbo and Pietro Pala, “Distinguishing Facial Features for Ethnicity based 3D Face Recognition” University of Firenze, 2010.

 

[74]. Stoica, P., Eykhoff, P., Janssen, P., and S¨oderstr¨om, T.: Model structure se- lection by cross-validation. International Journal of Control, 43 (6), 1841–1878 (1986).

 

[75]. Stone, M.: Cross-validatory choice and assessment of statistical prediction (with discussion). Journal of the Royal Statistical Society (Series B), 36, 111–133 (1974).

 

[76]. Stoyanova, R. and Brown, T. R.: Nmr spectral quantitation by principal com- ponent analysis. NMR in Biomedicine, 14 (4), 271–277 (2001).

 

[77]. Sylvester, J.J.: On the reduction of a bilineal quantic of the n th order to the form of a sum of n products by a double orthogonal substitution. Messenger of Mathematics, 19, 42–46 (1889).

 

[78]. S. Sosnowski, C. Mayer, K. Kühnlenz, B. Radig), In The Thirty Sixth Annual Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB2010), volume , 2010.

 

[79]. Tan, S. and Mavrovouniotis, M.L.: Reducing data dimensionality through opti- mizing neural network inputs. AIChE Journal, 41 (6), 1471–1480 (1995).

 

[80]. Thai Hoang Le, “Applying Artificial Neural Networks for Face Recognition”, Department of Computer Science, Ho Chi Minh University of Science Ho Chi Minh City, Vietnam. 2006.

 

[81]. Trafalis, T.B., Richman, M.B., White, A., and Santosa, B.: Data mining tech- niques for improved wsr-88d rainfall estimation. Computers & Industrial Engi- neering, 43 (4), 775–786 (2002).

 

[82]. T.F. Cootes and C.J. Taylor. “Statistical Models of Appearance for Computer Vision”, Imaging Science and Biomedical Engineering, University of Manchester, March 2004.

 

[83]. T.F. Cootes and C.J.Taylor, “Statistical Models of Appearance for Computer Vision”, Imaging Science and Biomedical Engineering, Cap 7.

 

[84]. Uffel, S. van, and Vandewalle, J.: The Total Least Squares Problem: Compu- tational Aspects and Analysis. SIAM, Philadelphia (1991).

 

[85]. Uricar Michal.” Detector of face landmark” Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University, 2011.

 

[86]. Uricar Michal.” Detector of landmark points on human face” Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University, 2011.

 

[87]. Vaswani, N. and Chellappa, R.: Principal components null space analysis for image and video classification. IEEE Transactions on Image Processing, 15 (7), 1816–1830 (2006).

 

[88]. Volker Blanz, Thomas Vetter, “A Morphable Model For The Synthesis Of 3D Faces", Max-Planck-Institut fur biologische Kybernetik, T¨ubingen, Germany, 1999.

 

[89]. Wax, M. and Kailath, T.: Detection of signals by information theoretic criteria. IEEE Transactions on Acoustics, Speech & Signal Processing, 33 (2), 387–392 (1985).

 

[90]. Wen-Bing Horng, Cheng-Ping Lee and Chun-Wen Chen., " Classification of Age Groups Based on Facial Features", Tamkang Journal of Science and Engineering, Vol. 4, No. 3, pp. 183-192, 2001.

 

[91]. Wise, B.M. and Ricker, N.L.: Identification of finite impulse respnose models by principal components regression: Frequency response properties. Process Control & Quality, 4, 77–86 (1992).

 

[92]. Wold, H.: Estimation of principal components and related models by itera- tive least squares. In: Krishnaiah, P.R. (ed.) Multivariate Analysis, 391–420. Academic Press, N.Y. (1966).

 

[93]. Wold, S.: Cross validatory estimation of the number of principal components in factor and principal component models. Technometrics, 20 (4), 397–406 (1978).

 

[94]. Wold, S., Esbensen, K., and Geladi, P.: Principal component analysis. Chemo- metrics and Intelligent Laboratory Systems, 2, 37–52 (1987).

 

[95]. Wilkes, Member, IEEE, Stan Franklin, Erdem Erdemir, Member, Stephen Gordon, Member, IEEE, Steve Strain, Karen Miller and Kazuhiko Kawamura, Fellow, IEEE, Heterogeneous Artificial Agents for Triage Nurse Assistance, University of Memphis, 2009.

 

[96]. Xiaozheng Zhang, Yongsheng Gao “Face recognition across pose: A review” Pattern Recognition 42 (2009) 2876 – 2896.

 

[97]. Yeongjae Cheon, Daijin Kim “Natural facial expression recognition using differential-AAM and manifold learning”, Pattern Recognition 42 (2009) 1340 – 1350.

 

[98]. Yoo, C.K. and Lee, I.: Nonlinear multivariate filtering and bioprocess monitoring for supervising nonLÍNEAr biological processes. Process Biochemistry, 41 (8), 1854–1863 (2006).

 

[99]. Zeng, Z. and Zou, X.: Application of principal component analysis to champ radio occultation data for quality control and a diagnostic study. Monthly Weather Review, 134 (11), 3263–3282 (2006).

[100]. J.Huang, Bernd Heisele, and Volker Blanz,"-based Face Recognition with 3D Morphable Models", 2001.