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An Empirical Comparison of SNoW and SVMs for Face Detection

dc.date.accessioned2004-10-20T20:50:07Z
dc.date.accessioned2018-11-24T10:23:25Z
dc.date.available2004-10-20T20:50:07Z
dc.date.available2018-11-24T10:23:25Z
dc.date.issued2001-01-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7219
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/7219
dc.description.abstractImpressive 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.en_US
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dc.format.extent319169 bytes
dc.language.isoen_US
dc.titleAn Empirical Comparison of SNoW and SVMs for Face Detectionen_US


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