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  Efficient face detection by a cascaded support-vector machine expansion

Romdhani, S., Torr P, Schölkopf, B., & Blake, A. (2004). Efficient face detection by a cascaded support-vector machine expansion. Proceedings of The Royal Society of London A, 460(2501), 3283-3297. doi:10.1098/rspa.2004.1333.

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Romdhani, S, Autor
Torr P, Schölkopf, B1, Autor           
Blake, A, Autor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: We describe a fast system for the detection and localization of human faces in images using a nonlinear ‘support-vector machine‘. We approximate the decision surface in terms of a reduced set of expansion vectors and propose a cascaded evaluation which has the property that the full support-vector expansion is only evaluated on the face-like parts of the image, while the largest part of typical images is classified using a single expansion vector (a simpler and more efficient classifier). As a result, only three reduced-set vectors are used, on average, to classify an image patch. Hence, the cascaded evaluation, presented in this paper, offers a thirtyfold speed-up over an evaluation using the full set of reduced-set vectors, which is itself already thirty times faster than classification using all the support vectors.

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 Datum: 2004-11
 Publikationsstatus: Erschienen
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Titel: Proceedings of The Royal Society of London A
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 460 (2501) Artikelnummer: - Start- / Endseite: 3283 - 3297 Identifikator: -