Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74339
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorChen, F-
dc.creatorZhang, D-
dc.creatorWang, C-
dc.creatorDuan, X-
dc.date.accessioned2018-03-29T07:16:37Z-
dc.date.available2018-03-29T07:16:37Z-
dc.identifier.isbn9783319699226-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10397/74339-
dc.description12th Chinese Conference on Biometric Recognition, CCBR 2017, 28 - 29 October 2017en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectFacial beautyen_US
dc.subjectFeature extractionen_US
dc.subjectFusionen_US
dc.titleComparison and fusion of multiple types of, features for image-based facial beauty, predictionen_US
dc.typeConference Paperen_US
dc.identifier.spage23-
dc.identifier.epage30-
dc.identifier.volume10568-
dc.identifier.doi10.1007/978-3-319-69923-3_3-
dcterms.abstractFacial beauty prediction is an emerging research topic that has many potential applications. Existing works adopt features either suggested by putative rules or borrowed from other face analysis tasks, without an optimization procedure. In this paper, we make a comprehensive comparison of different types of features in terms of facial beauty prediction accuracy, including the rule-based features, global features, and local descriptors. Each type of feature is optimized by dimensionality reduction and feature selection. Then, we investigate the optimal fusion strategy of multiple types of features. The results show that the fusion of AAM, LBP, and PCANet features obtains the best performance, which can serve as a competitive baseline for further studies.-
dcterms.bibliographicCitationLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10568, p. 23-30-
dcterms.isPartOfLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)-
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85032689205-
dc.relation.conferenceChinese Conference on Biometric Recognition [CCBR]-
dc.identifier.eissn1611-3349-
dc.description.validate201802 bcrc-
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