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Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7229
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dc.creatorHeisele, Bernd-
dc.creatorPoggio, Tomaso-
dc.creatorPontil, Massimiliano-
dc.date2004-10-20T21:03:29Z-
dc.date2004-10-20T21:03:29Z-
dc.date2000-05-01-
dc.date.accessioned2013-10-09T02:48:36Z-
dc.date.available2013-10-09T02:48:36Z-
dc.date.issued2013-10-09-
dc.identifierAIM-1687-
dc.identifierCBCL-187-
dc.identifierhttp://hdl.handle.net/1721.1/7229-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/1721-
dc.descriptionWe present a trainable system for detecting frontal and near-frontal views of faces in still gray images using Support Vector Machines (SVMs). We first consider the problem of detecting the whole face pattern by a single SVM classifer. In this context we compare different types of image features, present and evaluate a new method for reducing the number of features and discuss practical issues concerning the parameterization of SVMs and the selection of training data. The second part of the paper describes a component-based method for face detection consisting of a two-level hierarchy of SVM classifers. On the first level, component classifers independently detect components of a face, such as the eyes, the nose, and the mouth. On the second level, a single classifer checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face.-
dc.format6267853 bytes-
dc.format482304 bytes-
dc.formatapplication/postscript-
dc.formatapplication/pdf-
dc.languageen_US-
dc.relationAIM-1687-
dc.relationCBCL-187-
dc.titleFace Detection in Still Gray Images-
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