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Age and gender estimation using deep residual learning network
  • Lee, Seok Hee ;
  • Hosseini, Sepidehsadat ;
  • Kwon, Hyuk Jin ;
  • Moon, Jaewon ;
  • Koo, Hyung Il ;
  • Cho, Nam Ik
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dc.contributor.authorLee, Seok Hee-
dc.contributor.authorHosseini, Sepidehsadat-
dc.contributor.authorKwon, Hyuk Jin-
dc.contributor.authorMoon, Jaewon-
dc.contributor.authorKoo, Hyung Il-
dc.contributor.authorCho, Nam Ik-
dc.date.issued2018-05-30-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/36301-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85048817247&origin=inward-
dc.description.abstractIn this paper, we propose a deep residual learning model for age and gender estimation. Our method detects faces in input images, and then the age and gender of each face are estimated. The estimation method consists of three deep neural networks where we adopt residual learning methods. We train the model with IMDB-WIKI database [4]. However, since the database has only a small number of face images under the age of 20, we augment the set by collecting the images on the Internet. Experimental results show that the proposed model with residual learning yields improved performance.-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.subject.meshAge estimation-
dc.subject.meshEstimation methods-
dc.subject.meshGender estimations-
dc.subject.meshInput image-
dc.subject.meshLearning methods-
dc.subject.meshLearning models-
dc.subject.meshLearning network-
dc.subject.meshResidual learning-
dc.titleAge and gender estimation using deep residual learning network-
dc.typeConference-
dc.citation.conferenceDate2018.1.7. ~ 2018.1.9.-
dc.citation.conferenceName2018 International Workshop on Advanced Image Technology, IWAIT 2018-
dc.citation.edition2018 International Workshop on Advanced Image Technology, IWAIT 2018-
dc.citation.endPage3-
dc.citation.startPage1-
dc.citation.title2018 International Workshop on Advanced Image Technology, IWAIT 2018-
dc.identifier.bibliographicCitation2018 International Workshop on Advanced Image Technology, IWAIT 2018, pp.1-3-
dc.identifier.doi10.1109/iwait.2018.8369763-
dc.identifier.scopusid2-s2.0-85048817247-
dc.identifier.urlhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8365178-
dc.subject.keywordAge estimation-
dc.subject.keywordDeep learning-
dc.subject.keywordGender estimation-
dc.subject.keywordImage processing-
dc.subject.keywordMachine learning-
dc.subject.keywordResidual learning-
dc.type.otherConference Paper-
dc.description.isoafalse-
dc.subject.subareaComputer Networks and Communications-
dc.subject.subareaComputer Vision and Pattern Recognition-
dc.subject.subareaMedia Technology-
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KOO, HYUNG IL구형일
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