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dc.contributor.authorKim, Junghoon-
dc.contributor.authorYun, Sang-Seok-
dc.contributor.authorChoi, Jongsuk-
dc.contributor.authorKim, Daijin-
dc.contributor.authorKang, Bong-Nam-
dc.date.accessioned2024-01-19T11:07:58Z-
dc.date.available2024-01-19T11:07:58Z-
dc.date.created2022-02-28-
dc.date.issued2017-06-
dc.identifier.issn2325-033X-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/114639-
dc.description.abstractRecently, deep convolutional neural networks (DCNNs) have set a new trend in the computer vision community by improving the state-of-the-art performance in almost all of applications. We propose DCNN-based face recognition algorithm. This paper aims at analyzing and verifying considerations when the proposed method is implemented in a real environment. First, Multiple images of the same scene are processed. Also, this novel method is considered together with a method to reduce the total recognition time in the perspective of integrated system. We analyzed the experimental data and evaluated the performance by setting up the sensor fusion network in actual classroom.-
dc.languageEnglish-
dc.publisherIEEE-
dc.titleReliable Multi-Person Identification Using DCNN-Based Face Recognition Algorithm and Scale-Ratio Method-
dc.typeConference-
dc.description.journalClass1-
dc.identifier.bibliographicCitation14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp.97 - 101-
dc.citation.title14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)-
dc.citation.startPage97-
dc.citation.endPage101-
dc.citation.conferencePlaceUS-
dc.citation.conferencePlaceJeju, SOUTH KOREA-
dc.citation.conferenceDate2017-06-28-
dc.relation.isPartOf2017 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI)-
dc.identifier.wosid000426976900021-
dc.identifier.scopusid2-s2.0-85034246604-
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KIST Conference Paper > 2017
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