Holistic Face Recognition using Multivariate Approximation, Genetic Algorithms and AdaBoost Classifier: Preliminary Results
Several works regarding facial recognition have dealt with methods which identify isolated characteristics of the face or with templates which encompass several regions of it. In this paper a new technique which approaches the problem holistically dispensing with the need to identify geometrical characteristics or regions of the face is introduced. The characterization of a face is achieved by randomly sampling selected attributes of the pixels of its image. From this information we construct a set of data, which correspond to the values of low frequencies, gradient, entropy and another several characteristics of pixel of the image. Generating a set of “p" variables. The multivariate data set with different polynomials minimizing the data fitness error in the minimax sense (L∞ - Norm) is approximated. With the use of a Genetic Algorithm (GA) it is able to circumvent the problem of dimensionality inherent to higher degree polynomial approximations. The GA yields the degree and values of a set of coefficients of the polynomials approximating of the image of a face. By finding a family of characteristic polynomials from several variables (pixel characteristics) for each face (say Fi ) in the data base through a resampling process the system in use, is trained. A face (say F ) is recognized by finding its characteristic polynomials and using an AdaBoost Classifier from F -s polynomials to each of the Fi -s polynomials. The winner is the polynomial family closer to F -s corresponding to target face in data base.
[1] Belhumeur, P., Hespanha, J., Kriegman, D., "Eigenfaces vs Fisherfaces:
Recognition Using Class Specific Linear Projection", IEEE Trans.
Pattern recognition and Machine Intelligence, v. 19, No. 7, 1997, pp.
711-720.
[2] Bruce, Vicki, Hancock, Peter J.B., Burton, A. Mike, "Human Face
Perception and Identification", in: Wechsler, Harry, Phillips,
[3] Bruce, Vicki, et. al. (Eds.), Face Recognition: From Theory to
Applications, Springer/NATO, Germany, 1998.
[4] Brunelli, Roberto; Poggio, Tomaso; "Face Recognition through
Geometrical Features", McGraw Hill, 1995.
[5] Brunelli, Roberto; Poggio, Tomaso; "Face Recognition: Features versus
Templates"; IEEE Trans. on Pattern Recognition and Machine
Intelligence; v. 15; No. 10; October; 1993; pp. 1042-1052
[6] Cherkassky, Vladimir, "Inductive Principles for Learning from Data",
in: Wechsler, Harry, Phillips, P. J., Bruce, Vicki, et. al. (Eds.), Face
Recognition: From Theory to Applications, Springer/NATO, Germany,
1998.
[7] Costen, N.P., Parker, D.M., Craw, I., "Effects of high-pass and low-pass
spatial filtering on face identification", Perception & Psychophysics, v.
58, 1996, pp. 602-612.
[8] Cox, Ingemar J, Ghosn, J., Joumana, Y., "Feature-Based Face
Recognition Using Mixture-Distance"" NEC Research Institute,
Technical Report 95-09, Princeton, NJ, October, 1995.
[9] Dailey, Matthew N., Cottrell, Garrison W., "Learning a Specialization
for Face Recognition: The Effect of Spatial Frequency", June, 1997, in
Internet
[10] Ellis, H.D., "Introduction to aspects of face processing: Ten questions in
need of answers", In H. Ellis, M. Jeeves, F. Newcombe, eds., Aspects of
Face Processing, pp. 3-13, Nijhoff, 1996.
[11] Freund, Y., Schapire, R.E., "A decision-theoretic generalization of online
learning and an application to boosting", Journal of Computer and
Systems Sciences, vol. 55 (1), pp. 119-139, 1997.
[12] Gong, Shaogang, McKenna, Stephen J., Psarrou, Alexandra, Dynamic
Vision: From Images to Face Recognition, Imperial College Press,
London, 2000.
[13] Grotschel, Martin, Lovász, Lászlo, Combinatorial Optimization: A
Survey, DIMACS Technical Report 93-29, Princeton University, May,
1993. In Internet.
[14] Hancock, P.J., Bruce, V., Burton, A.M., "Testing Principal Component
Representation for faces", Technical report, University of Stirling, UK,
1998, in Internet.
[15] Hancock, Peter J. B.; Burton, A. Mike; Bruce, Vicki; "Face processing:
human perception and principal component analysis"; Memory and
Cognition; vol. 24; No. 1; 1996; pp 26-40.
[16] Howell, J., Buxton, H., "Invariance in radial basis function neural
networks in human face classification". Neural Processing Letters, 2(3),
pp. 26-30, 1995.
[17] Huang, Ren-Jay, Detection Strategies for face Recognition Using
Learning and Evolution, Ph. D. Dissertation, George Mason University,
Abstract, 1998.
[18] Isaka, Satoru, "An Empirical Study of Facial Image Feature Extraction
by Genetic Programming", Report- OMRON Advanced Systems, Inc.,
Santa Clara, CA, 1997, in Internet.
[19] Kuri, A., "A Methodology for the Statistical Characterization of Genetic
Algorithms", MICAI 2002: Advances in Artificial Intelligence, Lecture
Notes in Artificial Intelligence, pp. 79-88, Springer-Verlag, 2002.
[20] Kuri, A., Villegas, C., "A universal Eclectic Genetic Algorithm for
Constrained Optimization". Proceedings 6th European Congress on
Intelligent Techniques & Soft Computing, EUFIT'98, pp. 518-522, 1998.
[21] Kuri, Angel, "Pattern Recognition via a Genetic Algorithm", in Guzmán,
A., Shulcloper, J.R., Sossa, J.H., et al. (Comp.), II Taller Iberoamericano
de Reconocimiento de Patrones-La Habana, Cuba, ICIMAF-CICIPN,
1997, pp. 345-356.
[22] Lanitis, A.; Hill, A.; Cootes, T. F.; Taylor, C. J.; "Locating Facial
Features Using Genetic Algorithms"; Oxford;
[23] Lawrence, S., Giles, C. L., Tsoi, A., Back, A.D., "Face recognition: A
convolutional neural network approach", IEEE Transactions on Neural
Networks, 8(1), pp. 98-113, 1998.
[24] Laurenz, W., Fellous, J.M., Kr├╝ger, N., von der Malsburg, C., "Face
recognition by elastic bunch graph matching", 19 (7), pp. 775-779, 1997.
[25] Liu, Chengjun, Wechsler, Harry, "Face Recognition Using Evolutionary
Pursuit", Fifth European Conference on Computer Vision, University of
Freiburg, Germany, 1998, in Internet
[26] Liu, Chengjun, Wechsler, Harry, "Enhanced Fisher Linear Discriminant
Models for Face Recognition", 14
th
International Conference on Patter
Recognition , Queensland, Australia, 1998, in Internet.
[27] Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N., "Ensembles-based
Discriminant Learning with Boosting For Face Recognition", 2005, In
Internet
[28] Lu, X., Jain, A., "Resampling for Face Recognition", In Internet.
[29] Moghaddam, B., Pentland, A., "Probabilistic Visual Learning for Object
Representation", IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 18, pp. 696-710, 1997.
[30] Osuna, E., Freund, R., Girosi, F., Training support vector machines: An
application to face detection. 1997.
[31] Penev, P., Atick, J., Local Feature Analysis: A general statistical theory
for object representation, 1996.
[32] Pinto-Elías, R., Sossa-Azuela, J.H., "Human Face Identification Using
Invariant Descriptions and a Genetic Algorithm", in Coelho H. (Ed.),
Progress in Artificial Intelligence-IBERAMIA 98 (6
th
Ibero-American
Conference on AI-Lisbon, Portugal), Springer, Lecture Notes in AI-No.
1484, Germany, 1998, pp.293-302.
[33] Samaria, F.S., Harter, A.C., "Parameterization of a Stochastic Model for
Human Face Identification", Proceedings of the 2
nd
IEEE Workshop on
Application of Computer Gong, Shaogang, McKenna, Stephen J.,
Psarrou, Alexandra, Dynamic Vision: From Images to Face Recognition,
Imperial College Press, London, 2000. Vision, Sarasota, Florida,
December 1994.
[34] Schackleton, Mark, "Learned Deformable Templates for Object
Recognition", IEEE GAs in Vision Colloquium, 1996, in Internet.
[35] Schapire, R.E., "The boosting approach to machine learning: An
overview", MSRI Workshop on Nonlinear Estimation and
Classification, Berkeley, CA, pp. 149-172, 2002.
[36] Schoelkopf, B., Smola, A., Muller, K.R., "Kernel principal components
analysis", Artificial Neural Networks, ICANN97, 1997
[37] Turk, M., Pentland, A., "Eigenfaces for recognition", Journal of
Cognitive Neuroscience, 3 (1), pp. 71-86, 1991
[38] Turk, M.A., Pentland, A.P., "Face Recognition Using Eigenfaces",
Proceedings IEEE Computer Society Conference on Computer Vision
and Pattern recognition, pp. 586-591, 1991.
[39] Vapnik, V. N., The nature of statistics learning theory. Springer Verlag.
Heidelberg. 1995
[40] Wechsler, Harry, Phillips, P. J., Bruce, Vicki, et. al. (Eds.), Face
Recognition: From Theory to Applications, Springer/NATO, Germany,
1998.
[41] Viola, Paul, Jones, Michael, "Rapid Object using a Boosted Cascade of
Simple Features", Conference on Computer Vision and Pattern
Recognition, 2001.
[1] Belhumeur, P., Hespanha, J., Kriegman, D., "Eigenfaces vs Fisherfaces:
Recognition Using Class Specific Linear Projection", IEEE Trans.
Pattern recognition and Machine Intelligence, v. 19, No. 7, 1997, pp.
711-720.
[2] Bruce, Vicki, Hancock, Peter J.B., Burton, A. Mike, "Human Face
Perception and Identification", in: Wechsler, Harry, Phillips,
[3] Bruce, Vicki, et. al. (Eds.), Face Recognition: From Theory to
Applications, Springer/NATO, Germany, 1998.
[4] Brunelli, Roberto; Poggio, Tomaso; "Face Recognition through
Geometrical Features", McGraw Hill, 1995.
[5] Brunelli, Roberto; Poggio, Tomaso; "Face Recognition: Features versus
Templates"; IEEE Trans. on Pattern Recognition and Machine
Intelligence; v. 15; No. 10; October; 1993; pp. 1042-1052
[6] Cherkassky, Vladimir, "Inductive Principles for Learning from Data",
in: Wechsler, Harry, Phillips, P. J., Bruce, Vicki, et. al. (Eds.), Face
Recognition: From Theory to Applications, Springer/NATO, Germany,
1998.
[7] Costen, N.P., Parker, D.M., Craw, I., "Effects of high-pass and low-pass
spatial filtering on face identification", Perception & Psychophysics, v.
58, 1996, pp. 602-612.
[8] Cox, Ingemar J, Ghosn, J., Joumana, Y., "Feature-Based Face
Recognition Using Mixture-Distance"" NEC Research Institute,
Technical Report 95-09, Princeton, NJ, October, 1995.
[9] Dailey, Matthew N., Cottrell, Garrison W., "Learning a Specialization
for Face Recognition: The Effect of Spatial Frequency", June, 1997, in
Internet
[10] Ellis, H.D., "Introduction to aspects of face processing: Ten questions in
need of answers", In H. Ellis, M. Jeeves, F. Newcombe, eds., Aspects of
Face Processing, pp. 3-13, Nijhoff, 1996.
[11] Freund, Y., Schapire, R.E., "A decision-theoretic generalization of online
learning and an application to boosting", Journal of Computer and
Systems Sciences, vol. 55 (1), pp. 119-139, 1997.
[12] Gong, Shaogang, McKenna, Stephen J., Psarrou, Alexandra, Dynamic
Vision: From Images to Face Recognition, Imperial College Press,
London, 2000.
[13] Grotschel, Martin, Lovász, Lászlo, Combinatorial Optimization: A
Survey, DIMACS Technical Report 93-29, Princeton University, May,
1993. In Internet.
[14] Hancock, P.J., Bruce, V., Burton, A.M., "Testing Principal Component
Representation for faces", Technical report, University of Stirling, UK,
1998, in Internet.
[15] Hancock, Peter J. B.; Burton, A. Mike; Bruce, Vicki; "Face processing:
human perception and principal component analysis"; Memory and
Cognition; vol. 24; No. 1; 1996; pp 26-40.
[16] Howell, J., Buxton, H., "Invariance in radial basis function neural
networks in human face classification". Neural Processing Letters, 2(3),
pp. 26-30, 1995.
[17] Huang, Ren-Jay, Detection Strategies for face Recognition Using
Learning and Evolution, Ph. D. Dissertation, George Mason University,
Abstract, 1998.
[18] Isaka, Satoru, "An Empirical Study of Facial Image Feature Extraction
by Genetic Programming", Report- OMRON Advanced Systems, Inc.,
Santa Clara, CA, 1997, in Internet.
[19] Kuri, A., "A Methodology for the Statistical Characterization of Genetic
Algorithms", MICAI 2002: Advances in Artificial Intelligence, Lecture
Notes in Artificial Intelligence, pp. 79-88, Springer-Verlag, 2002.
[20] Kuri, A., Villegas, C., "A universal Eclectic Genetic Algorithm for
Constrained Optimization". Proceedings 6th European Congress on
Intelligent Techniques & Soft Computing, EUFIT'98, pp. 518-522, 1998.
[21] Kuri, Angel, "Pattern Recognition via a Genetic Algorithm", in Guzmán,
A., Shulcloper, J.R., Sossa, J.H., et al. (Comp.), II Taller Iberoamericano
de Reconocimiento de Patrones-La Habana, Cuba, ICIMAF-CICIPN,
1997, pp. 345-356.
[22] Lanitis, A.; Hill, A.; Cootes, T. F.; Taylor, C. J.; "Locating Facial
Features Using Genetic Algorithms"; Oxford;
[23] Lawrence, S., Giles, C. L., Tsoi, A., Back, A.D., "Face recognition: A
convolutional neural network approach", IEEE Transactions on Neural
Networks, 8(1), pp. 98-113, 1998.
[24] Laurenz, W., Fellous, J.M., Kr├╝ger, N., von der Malsburg, C., "Face
recognition by elastic bunch graph matching", 19 (7), pp. 775-779, 1997.
[25] Liu, Chengjun, Wechsler, Harry, "Face Recognition Using Evolutionary
Pursuit", Fifth European Conference on Computer Vision, University of
Freiburg, Germany, 1998, in Internet
[26] Liu, Chengjun, Wechsler, Harry, "Enhanced Fisher Linear Discriminant
Models for Face Recognition", 14
th
International Conference on Patter
Recognition , Queensland, Australia, 1998, in Internet.
[27] Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N., "Ensembles-based
Discriminant Learning with Boosting For Face Recognition", 2005, In
Internet
[28] Lu, X., Jain, A., "Resampling for Face Recognition", In Internet.
[29] Moghaddam, B., Pentland, A., "Probabilistic Visual Learning for Object
Representation", IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 18, pp. 696-710, 1997.
[30] Osuna, E., Freund, R., Girosi, F., Training support vector machines: An
application to face detection. 1997.
[31] Penev, P., Atick, J., Local Feature Analysis: A general statistical theory
for object representation, 1996.
[32] Pinto-Elías, R., Sossa-Azuela, J.H., "Human Face Identification Using
Invariant Descriptions and a Genetic Algorithm", in Coelho H. (Ed.),
Progress in Artificial Intelligence-IBERAMIA 98 (6
th
Ibero-American
Conference on AI-Lisbon, Portugal), Springer, Lecture Notes in AI-No.
1484, Germany, 1998, pp.293-302.
[33] Samaria, F.S., Harter, A.C., "Parameterization of a Stochastic Model for
Human Face Identification", Proceedings of the 2
nd
IEEE Workshop on
Application of Computer Gong, Shaogang, McKenna, Stephen J.,
Psarrou, Alexandra, Dynamic Vision: From Images to Face Recognition,
Imperial College Press, London, 2000. Vision, Sarasota, Florida,
December 1994.
[34] Schackleton, Mark, "Learned Deformable Templates for Object
Recognition", IEEE GAs in Vision Colloquium, 1996, in Internet.
[35] Schapire, R.E., "The boosting approach to machine learning: An
overview", MSRI Workshop on Nonlinear Estimation and
Classification, Berkeley, CA, pp. 149-172, 2002.
[36] Schoelkopf, B., Smola, A., Muller, K.R., "Kernel principal components
analysis", Artificial Neural Networks, ICANN97, 1997
[37] Turk, M., Pentland, A., "Eigenfaces for recognition", Journal of
Cognitive Neuroscience, 3 (1), pp. 71-86, 1991
[38] Turk, M.A., Pentland, A.P., "Face Recognition Using Eigenfaces",
Proceedings IEEE Computer Society Conference on Computer Vision
and Pattern recognition, pp. 586-591, 1991.
[39] Vapnik, V. N., The nature of statistics learning theory. Springer Verlag.
Heidelberg. 1995
[40] Wechsler, Harry, Phillips, P. J., Bruce, Vicki, et. al. (Eds.), Face
Recognition: From Theory to Applications, Springer/NATO, Germany,
1998.
[41] Viola, Paul, Jones, Michael, "Rapid Object using a Boosted Cascade of
Simple Features", Conference on Computer Vision and Pattern
Recognition, 2001.
@article{"International Journal of Information, Control and Computer Sciences:54694", author = "C. Villegas-Quezada and J. Climent", title = "Holistic Face Recognition using Multivariate Approximation, Genetic Algorithms and AdaBoost Classifier: Preliminary Results", abstract = "Several works regarding facial recognition have dealt with methods which identify isolated characteristics of the face or with templates which encompass several regions of it. In this paper a new technique which approaches the problem holistically dispensing with the need to identify geometrical characteristics or regions of the face is introduced. The characterization of a face is achieved by randomly sampling selected attributes of the pixels of its image. From this information we construct a set of data, which correspond to the values of low frequencies, gradient, entropy and another several characteristics of pixel of the image. Generating a set of “p" variables. The multivariate data set with different polynomials minimizing the data fitness error in the minimax sense (L∞ - Norm) is approximated. With the use of a Genetic Algorithm (GA) it is able to circumvent the problem of dimensionality inherent to higher degree polynomial approximations. The GA yields the degree and values of a set of coefficients of the polynomials approximating of the image of a face. By finding a family of characteristic polynomials from several variables (pixel characteristics) for each face (say Fi ) in the data base through a resampling process the system in use, is trained. A face (say F ) is recognized by finding its characteristic polynomials and using an AdaBoost Classifier from F -s polynomials to each of the Fi -s polynomials. The winner is the polynomial family closer to F -s corresponding to target face in data base.
", keywords = "AdaBoost Classifier, Holistic Face Recognition, Minimax Multivariate Approximation, Genetic Algorithm.", volume = "2", number = "8", pages = "2672-5", }
