Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5556
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dc.contributor.authorAydoğdu, M. Fatih-
dc.contributor.authorDemirci, Muhammed Fatih-
dc.date.accessioned2021-09-11T15:19:14Z-
dc.date.available2021-09-11T15:19:14Z-
dc.date.issued2017en_US
dc.identifier.citation2017 International Conference on Compute and Data Analysis, ICCDA 2017, 19 May 2017 through 23 May 2017, , 130280en_US
dc.identifier.isbn9781450352413-
dc.identifier.urihttps://doi.org/10.1145/3093241.3093277-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/5556-
dc.description.abstractWith growing data size in multimedia systems, the need for successful image classification and retrieval systems becomes vital. Nevertheless, the performance of such systems is still limited for real-world applications. In this paper, we propose an optimized Convolutional Neural Network (CNN) architecture for the age classification problem. In order to justify the structure and depth of the proposed CNN-based framework, comprehensive experiments on a number of different CNN architectures are conducted. Based on the fitness of the age classification results with respect to success-error ratios, training times, and standard deviations of success rates; using exact, top-3 and 1-off criterion, the CNN architecture involving 4 convolutional layers and 2 fully connected layers is found to be superior to the other CNN-based architectures with different number of layers. We evaluate our method on a face database consisting of more than 55,000 images. © 2017 Association for Computing Machinery.en_US
dc.description.sponsorshipUniversity of Floridaen_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofACM International Conference Proceeding Seriesen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAge estimationen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learningen_US
dc.titleAge Classification Using an Optimized Cnn Architectureen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volumePart F130280en_US
dc.identifier.startpage233en_US
dc.identifier.endpage239en_US
dc.identifier.scopus2-s2.0-85030123829en_US
dc.institutionauthorDemirci, Muhammed Fatih-
dc.identifier.doi10.1145/3093241.3093277-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference2017 International Conference on Compute and Data Analysis, ICCDA 2017en_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.3. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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