Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4367
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorChow, TY-
dc.creatorLam, KMK-
dc.creatorWong, KW-
dc.date.accessioned2014-12-11T08:23:34Z-
dc.date.available2014-12-11T08:23:34Z-
dc.identifier.issn1017-9909-
dc.identifier.urihttp://hdl.handle.net/10397/4367-
dc.language.isoenen_US
dc.publisherSPIE-International Society for Optical Engineeringen_US
dc.rightsCopyright 2006 Society of Photo-Optical Instrumentation Engineers and IS&T. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.en_US
dc.subjectFace recognitionen_US
dc.subjectImage colour analysisen_US
dc.subjectImage resolutionen_US
dc.subjectImage segmentationen_US
dc.subjectMaximum likelihood estimationen_US
dc.titleEfficient color face detection algorithm under different lighting conditionsen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: Kin-Man Lamen_US
dc.identifier.spage1-
dc.identifier.epage10-
dc.identifier.volume15-
dc.identifier.issue1-
dc.identifier.doi10.1117/1.2179080-
dcterms.abstractWe present an efficient and reliable algorithm to detect human faces in an image under different lighting conditions. In our algorithm, skin-colored pixels are identified using a region-based approach, which can provide more reliable skin color segmentation under various lighting conditions. In addition, to compensate for extreme lighting conditions, a color compensation scheme is proposed, and the distributions of the skin-color components under various illuminations are modeled by means of the maximum-likelihood method. With the skin-color regions detected, a ratio method is proposed to determine the possible positions of the eyes in the image. Two eye candidates form a possible face region, which is then verified as a face or not by means of a two-stage procedure with an eigenmask. Finally, the face boundary region of a face candidate is further verified by a probabilistic approach to reduce the chance of false alarms. Experimental results based on the HHI MPEG-7 face database, the AR face database, and the CMU pose, illumination, and expression (PIE) database show that this face detection algorithm is efficient and reliable under different lighting conditions and facial expressions.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of electronic imaging, Jan.-Mar. 2006, v. 15, no. 1, 013015, p. 1-10-
dcterms.isPartOfJournal of electronic imaging-
dcterms.issued2006-01-
dc.identifier.isiWOS:000237622800017-
dc.identifier.scopus2-s2.0-33744544664-
dc.identifier.eissn1560-229X-
dc.identifier.rosgroupidr28924-
dc.description.ros2005-2006 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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