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Facial feature point extraction using the adaptive mean shape in active shape model

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dc.contributor.authorKim, Hyun-Chul-
dc.contributor.authorKim, Hyoung-Joon-
dc.contributor.authorHwang, Wonjun-
dc.contributor.authorKee, Seok-Cheol-
dc.contributor.authorKim, Whoi Yul-
dc.date.accessioned2022-12-21T08:56:41Z-
dc.date.available2022-12-21T08:56:41Z-
dc.date.created2022-09-16-
dc.date.issued2007-03-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180353-
dc.description.abstractThe fixed mean shape that is built from the statistical shape model produces an erroneous feature extraction result when ASM is applied to multipose faces. To remedy this problem the mean shape vector which is similar to an input face image is needed. In this paper, we propose the adaptive mean shape to extract facial features accurately for non frontal face. It indicates the mean shape vector that is the most similar to the face form of the input image. Our experimental results show that the proposed method obtains feature point positions with high accuracy and significantly improving the performance of facial feature extraction over and above that of the original ASM-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleFacial feature point extraction using the adaptive mean shape in active shape model-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Whoi Yul-
dc.identifier.doi10.1007/978-3-540-71457-6_38-
dc.identifier.scopusid2-s2.0-37149038081-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4418 LNCS, pp.421 - 429-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume4418 LNCS-
dc.citation.startPage421-
dc.citation.endPage429-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusAdaptive systems-
dc.subject.keywordPlusFeature extraction-
dc.subject.keywordPlusProblem solving-
dc.subject.keywordPlusStatistical methods-
dc.subject.keywordPlusVectors-
dc.subject.keywordPlusActive shape model-
dc.subject.keywordPlusFacial feature extraction-
dc.subject.keywordPlusMultipose faces-
dc.subject.keywordPlusFace recognition-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-540-71457-6_38-
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서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles

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