Multimodal Face Biometrics by Using Convolutional Neural Networks

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dc.contributor.authorLeslie Ching Ow Tiongko
dc.contributor.authorKim, Seong Taeko
dc.contributor.authorRo, Yong Manko
dc.date.accessioned2017-08-16T08:53:56Z-
dc.date.available2017-08-16T08:53:56Z-
dc.date.created2017-05-15-
dc.date.created2017-05-15-
dc.date.issued2017-02-
dc.identifier.citation멀티미디어학회논문지, v.20, no.2, pp.170 - 178-
dc.identifier.issn1229-7771-
dc.identifier.urihttp://hdl.handle.net/10203/225350-
dc.description.abstractBiometric recognition is one of the major challenging topics which needs high performance of recognition accuracy. Most of existing methods rely on a single source of biometric to achieve recognition. The recognition accuracy in biometrics is affected by the variability of effects, including illumination and appearance variations. In this paper, we propose a new multimodal biometrics recognition using convolutional neural network. We focus on multimodal biometrics from face and periocular regions. Through experiments, we have demonstrated that facial multimodal biometrics features deep learning framework is helpful for achieving high recognition performance.-
dc.languageEnglish-
dc.publisher한국멀티미디어학회-
dc.subjectMultimodal Biometrics Recognition-
dc.subjectFace Recognition-
dc.subjectConvolutional Neural Networks-
dc.titleMultimodal Face Biometrics by Using Convolutional Neural Networks-
dc.typeArticle-
dc.type.rimsART-
dc.citation.volume20-
dc.citation.issue2-
dc.citation.beginningpage170-
dc.citation.endingpage178-
dc.citation.publicationname멀티미디어학회논문지-
dc.identifier.kciidART002203545-
dc.contributor.localauthorRo, Yong Man-
dc.contributor.nonIdAuthorLeslie Ching Ow Tiong-
dc.description.isOpenAccessN-
dc.subject.keywordAuthorMultimodal Biometrics Recognition-
dc.subject.keywordAuthorFace Recognition-
dc.subject.keywordAuthorConvolutional Neural Networks-
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