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dc.contributor.authorSaber, Eli
dc.contributor.authorTekalp, A. Murat
dc.date.accessioned2009-06-17T19:12:50Z
dc.date.available2009-06-17T19:12:50Z
dc.date.issued1998
dc.identifier.issn0167-8655
dc.identifier.urihttp://hdl.handle.net/1850/9865
dc.descriptionRIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/
dc.description.abstractWe describe an algorithm for detecting human faces and facial features, such as the location of the eyes, nose and mouth. First, a supervised pixel-based color classifier is employed to mark all pixels that are within a prespecified distance of ‘‘skin color’’, which is computed from a training set of skin patches. This color-classification map is then smoothed by Gibbs random field model-based filters to define skin regions. An ellipse model is fit to each disjoint skin region. Finally, we introduce symmetry-based cost functions to search the center of the eyes, tip of nose, and center of mouth within ellipses whose aspect ratio is similar to that of a face.en_US
dc.language.isoen_USen_US
dc.publisherElsveier - Pattern Recognition Lettersen_US
dc.relation.ispartofseriesVol. 19en_US
dc.relation.ispartofseriesNo. 8en_US
dc.subjectFace detectionen_US
dc.subjectFacial feature detectionen_US
dc.subjectGibbs random fieldsen_US
dc.subjectImage segmentationen_US
dc.subjectShape classificationen_US
dc.titleFrontal-view face detection and facial feature extraction using color, shape and symmetry based cost functionsen_US
dc.typeArticleen_US
dc.identifier.urlhttp://dx.doi.org/10.1016/S0167-8655(98)00044-0


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