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.
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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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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