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An Empirical Comparison of SNoW and SVMs for Face Detection

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dc.creator Alvira, Mariano
dc.creator Rifkin, Ryan
dc.date 2004-10-20T20:50:07Z
dc.date 2004-10-20T20:50:07Z
dc.date 2001-01-01
dc.date.accessioned 2013-10-09T02:48:35Z
dc.date.available 2013-10-09T02:48:35Z
dc.date.issued 2013-10-09
dc.identifier AIM-2001-004
dc.identifier CBCL-193
dc.identifier http://hdl.handle.net/1721.1/7219
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description Impressive claims have been made for the performance of the SNoW algorithm on face detection tasks by Yang et. al. [7]. In particular, by looking at both their results and those of Heisele et. al. [3], one could infer that the SNoW system performed substantially better than an SVM-based system, even when the SVM used a polynomial kernel and the SNoW system used a particularly simplistic 'primitive' linear representation. We evaluated the two approaches in a controlled experiment, looking directly at performance on a simple, fixed-sized test set, isolating out 'infrastructure' issues related to detecting faces at various scales in large images. We found that SNoW performed about as well as linear SVMs, and substantially worse than polynomial SVMs.
dc.format 1232391 bytes
dc.format 319169 bytes
dc.format application/postscript
dc.format application/pdf
dc.language en_US
dc.relation AIM-2001-004
dc.relation CBCL-193
dc.title An Empirical Comparison of SNoW and SVMs for Face Detection


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