Análise de Discriminantes Lineares para Modelagem e Reconstrução de Imagens de Face
Resumo
Muitas abordagens que trabalham com classificadores lineares normalmente consideram o uso do hiperplano de separação apenas para fins de classificação. Poucos foram os trabalhos que estudaram a possibilidade de um classificador linear fornecer muito mais informações do que somente a base vetorial ótima que separa dois ou mais conjuntos distintos de observações. Este trabalho aborda uma extensão do uso de um classificador linear, explorando e visualizando as informações discriminantes que o classificador captura, bem como a sua capacidade de generalização e o potencial de predição da variabilidade contida em um conjunto de treinamento composto por imagens de faces.Referências
BELHUMEUR, P.; HESPANHA, J. P. N.; KRIEGMAN, D. J. Eigenfaces vs. Fisher-faces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 19, n. 7, pp. 711-720, July 1997.
BUCHALA, S.; DAVEY, N.; GALE, T. M.; FRANK, R. J. Principal components analysis of gender, ethnicity, age and identity of face images. In 5th annual UK Workshop on Computational Intelligence, London, UK, 8 pages, November 2005.
COOTES, T. F.; EDWARDS, G. J.; TAYLOR, C. J. Active Appearance Models. H. BURKHARDT and B. NEWMANN, ECCV, Springer, pp. 484-498, 1998.
EDWARDS, G. J.; LANITIS, A.; TAYLOR, C. J.; COOTES, T. F.; Statistical models of face images – Improving specificity. Image and Vision Computing; v. 16, pp. 203-211, 1998.
FUKUNAGA, K. Introduction to statistical pattern recognition, 2nd ed. Academic Press, Boston, 1990.
KITANI, E. C.; THOMAZ, C. E.; GILLIES, D. F. A statistical discriminant model for face interpretation and reconstruction. In: proceedings of SIBGRAPI' 06, IEEE CS Press, Manaus, Amazonas, Brazil, pp. 247-254, October 2006.
JOLLIFFE, I. T. Principal components analysis, UK: 2nd ed., Springer, 2002.
LASCA, V. B.; BORGES, D. L. Recognizing Faces with Minimum Information. In proceedings of Signal Processing, Pattern Recognition, and Applications,SPPRA, pp. 260-265, 2002.
SIROVICJH, L.; KIRBY, M. Low-dimensional procedure for the characterization of human faces. Journal of Optical Society of America, v. 4, pp. 519-524, March 1987.
OLIVEIRA JR., L. L.; THOMAZ, C. E.; Captura e alinhamento de imagens: Um banco de faces brasileiro. Relatório de iniciação científica, Depto. Eng. Elétrica da FEI, São Bernardo do Campo, SP, 10 f., 2006, disponível em [link].
SWETS, D.; WENG, J. Using discriminant eigenfeatures for image retrieval. IEEE PAMI, v. 18, n. 8, pp. 831-836, August 1996.
THOMAZ, C. E. GILLIES, D. F., FEITOSA, R. Q., A new covariance estimate for Bayesian classifiers in biometric recognition, IEEE Transactions on circuits and system video-based biometrics, v. 14, no. 2, pp. 214-223, February 2004.
THOMAZ, C. E.; GILLIES, D. F.; A maximum uncertainty LDA-based approach for limited sample size problems with application to face recognition. In Proceedings of SIBGRAPI’ 05, IEEE CS Press, pp. 89-96, 2005.
THOMAZ, C. E., KITANI, E. C. GILLIES, D. F., A maximun uncertainty LDA-based approach for limited sample size problems with application to face recognition, extended version of the SIBGRAPI’05 paper, Journal of the Brazilian Computer Society (JBCS), vol. 12, no. 2, pp. 7-18, September 2006.
TURK, M.; PENTLAND, A. Eigenfaces for recognition. Journal of Cognitive Neuroscience, MIT, v. 3, n. 1, pp. 71-86, March 1991.
BUCHALA, S.; DAVEY, N.; GALE, T. M.; FRANK, R. J. Principal components analysis of gender, ethnicity, age and identity of face images. In 5th annual UK Workshop on Computational Intelligence, London, UK, 8 pages, November 2005.
COOTES, T. F.; EDWARDS, G. J.; TAYLOR, C. J. Active Appearance Models. H. BURKHARDT and B. NEWMANN, ECCV, Springer, pp. 484-498, 1998.
EDWARDS, G. J.; LANITIS, A.; TAYLOR, C. J.; COOTES, T. F.; Statistical models of face images – Improving specificity. Image and Vision Computing; v. 16, pp. 203-211, 1998.
FUKUNAGA, K. Introduction to statistical pattern recognition, 2nd ed. Academic Press, Boston, 1990.
KITANI, E. C.; THOMAZ, C. E.; GILLIES, D. F. A statistical discriminant model for face interpretation and reconstruction. In: proceedings of SIBGRAPI' 06, IEEE CS Press, Manaus, Amazonas, Brazil, pp. 247-254, October 2006.
JOLLIFFE, I. T. Principal components analysis, UK: 2nd ed., Springer, 2002.
LASCA, V. B.; BORGES, D. L. Recognizing Faces with Minimum Information. In proceedings of Signal Processing, Pattern Recognition, and Applications,SPPRA, pp. 260-265, 2002.
SIROVICJH, L.; KIRBY, M. Low-dimensional procedure for the characterization of human faces. Journal of Optical Society of America, v. 4, pp. 519-524, March 1987.
OLIVEIRA JR., L. L.; THOMAZ, C. E.; Captura e alinhamento de imagens: Um banco de faces brasileiro. Relatório de iniciação científica, Depto. Eng. Elétrica da FEI, São Bernardo do Campo, SP, 10 f., 2006, disponível em [link].
SWETS, D.; WENG, J. Using discriminant eigenfeatures for image retrieval. IEEE PAMI, v. 18, n. 8, pp. 831-836, August 1996.
THOMAZ, C. E. GILLIES, D. F., FEITOSA, R. Q., A new covariance estimate for Bayesian classifiers in biometric recognition, IEEE Transactions on circuits and system video-based biometrics, v. 14, no. 2, pp. 214-223, February 2004.
THOMAZ, C. E.; GILLIES, D. F.; A maximum uncertainty LDA-based approach for limited sample size problems with application to face recognition. In Proceedings of SIBGRAPI’ 05, IEEE CS Press, pp. 89-96, 2005.
THOMAZ, C. E., KITANI, E. C. GILLIES, D. F., A maximun uncertainty LDA-based approach for limited sample size problems with application to face recognition, extended version of the SIBGRAPI’05 paper, Journal of the Brazilian Computer Society (JBCS), vol. 12, no. 2, pp. 7-18, September 2006.
TURK, M.; PENTLAND, A. Eigenfaces for recognition. Journal of Cognitive Neuroscience, MIT, v. 3, n. 1, pp. 71-86, March 1991.
Publicado
30/06/2007
Como Citar
KITANI, Edson C.; THOMAZ, Carlos E..
Análise de Discriminantes Lineares para Modelagem e Reconstrução de Imagens de Face. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 6. , 2007, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2007
.
p. 962-971.
ISSN 2763-9061.
