Abstract
The problem of high dimensionality in face verification tasks has recently been simplified by the use of underlying spatial structures as proposed in the 2DPCA, 2DLDA and CSA methods. Fusion techniques at both levels, feature extraction and matching score, have been developed to join the information obtained and achieve better results in verification process. The application of these advances to facial verification techniques using different SVM schemes as classification algorithm is here shown. The experiments have been performed over a wide facial database (FRAV2D including 109 subjects), in which only one interest variable was changed in each experiment. For training the SVMs, only two images per subject have been provided to fit in the small sample size problem.
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References
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neurosicience 3, 71–86 (1999)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)
Fortuna, J., Capson, D.: Improved support vector classification using PCA and ICA feature space modiffication. Pattern Recognition 37, 1117–1129 (2004)
Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)
Guo, G., Dyer, C.: Learning from examples in the small sample case: face expression recognition. IEEE Transactions on Systems, Man, and Cybernetics-Part B 35, 477–488 (2005)
Pang, S., Kim, D., Bang, S.Y.: Memebership authentication in the dynamic group by face classification using SVM ensemble. Pattern Recognition Letters 24, 215–225 (2003)
Yang, J., Yang, J.: From image vector to matrix: a straightforward image projection technique–IMPCA vs. PCA. Pattern Recognition 35, 1997–1999 (2002)
Yang, J., Zhang, D., Frangi, F., Yang, J.: Two-dimmensional PCA: A new approach to apperance-based face representation and recognition. IEEE Transacctions on Pattern Analysis and Machine Intelligence 26, 131–137 (2004)
Chen, S., Zhu, Y., Zhang, D., Yang, J.: Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters 26, 1157–1167 (2005)
Li, M., Yuan, B.: A novel statistical linear discriminant analysis for image matrix: two-dimensional fisherfaces. In: Proceedings of the International Conference on Signal Processing, pp. 1419–1422 (2004)
Xu, D., Yan, S., Zhang, L., Liu, Z., Zhang, H.: Coupled subspace analysis. Technical Report MSR-TR-2004-106, Microsof Research (2004)
Ye, J.: Generalized low rank approximations of matrices. Machine Learning 61, 167–191 (2005)
Cortes, C., Vapnik, V.: Support vector network. Machine Learning 20, 273–297 (1995)
Joachims, T.: Making large scale support vector machine learning practical. In: Advances in Kernel Methods: Support Vector Machines, MIT Press, Cambridge (1998)
Liu, J., Chen, S.: Non-iterative generalized low rank approximation of matrices. Pattern Recognition Letters 27, 1002–1008 (2006)
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© 2006 Springer-Verlag Berlin Heidelberg
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Rodríguez-Aragón, L.J., Conde, C., Serrano, Á., Cabello, E. (2006). Comparing and Combining Spatial Dimension Reduction Methods in Face Verification. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_78
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DOI: https://doi.org/10.1007/11875581_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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