Abstract
The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of eigenfaces to achieve a good classification rate and how to train the SVM for a good generalization. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies for choosing the dimensionality of the PCA space and discuss their effectiveness in the case of face-class modeling.
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Work partially performed in the BANCA project of the IST European program with the financial support of the Swiss OFES and with the support of the IM2-NCCR of the Swiss NFS.
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References
Hotteling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology (1933) 417–441, 498–520
Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A 4 (1987) 519–524
Turk, M. A., Pentland, A.P.: Face recognition using eigenfaces. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition. (1991) 586–591
Zhao, W., Chellappa, R., Krishaswamy, A.: Discriminant analysis of principal components for face recognition. In: Proceedings of the 3rd International Conference on Automatic Face and Gesture Recognition. (1998)
Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Technical Report NCRG/97/010, Neural Computing Research Group, Dept. of Computer Science and Applied Mathematics, Aston University (1997)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object detection. In: Proc. of the 5th International Conference on Computer Vision. (1995) 786–793
Minka, T. P.: Automatic choice of dimensionality for PCA. Technical Report 514, M. I. T. Media Laboratory Perceptual Computing Section (2000)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3 (1991) 71–86
Vapnik, V.: The Nature of Statistical Learning Theory. Springer Verlag (1995)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press (2000)
Bengio, S., Bimbot, F., Mariéthoz, J., Popovici, V., Porée, F., Bailly-Baillière, E., Matas, G., Ruiz, B.: Experimental protocol on the BANCA database. IDIAPRR 05, IDIAP (2002)
Sung, K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 20 (1998) 39–51
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceedings of Intl. Conference on Computer Vision and Pattern Recognition (CVPR). (1997)
Penev, P. S., Sirovich, L.: The global dimensionality of face space. In: Proceedings of the 4th Intl. Conference on Automatic Face and Gesture Recognition, IEEE CS (2000) 264–270
Roth, V., Steinhage, V.: Nonlinear discriminant analysis using kernel functions. In Solla, S., Leen, T., Müller, K.R., eds.: Advances in Neural Information Processing Systems. Volume 12., MIT Press (1999) 568–574
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Popovici, V., Thiran, JP. (2003). Face Detection Using an SVM Trained in Eigenfaces Space. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_23
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DOI: https://doi.org/10.1007/3-540-44887-X_23
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