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Gender recognition on real world faces based on shape representation and neural network

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dc.citedby9
dc.contributor.authorArigbabu O.A.en_US
dc.contributor.authorAhmad S.M.S.en_US
dc.contributor.authorAdnan W.A.W.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorIranmanesh V.en_US
dc.contributor.authorMalallah F.L.en_US
dc.contributor.authorid56047591000en_US
dc.contributor.authorid24721182400en_US
dc.contributor.authorid6506665562en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid56047920000en_US
dc.contributor.authorid56102103900en_US
dc.date.accessioned2023-05-16T02:45:51Z
dc.date.available2023-05-16T02:45:51Z
dc.date.issued2014
dc.description.abstractGender as a soft biometric attribute has been extensively investigated in the domain of computer vision because of its numerous potential application areas. However, studies have shown that gender recognition performance can be hindered by improper alignment of facial images. As a result, previous experiments have adopted face alignment as an important stage in the recognition process, before performing feature extraction. In this paper, the problem of recognizing human gender from unaligned real world faces using single image per individual is investigated. The use of feature descriptor to form shape representation of face images with any arbitrary orientation from the cropped version of Labeled Faces in the Wild (LFW) dataset is proposed. By combining the feature extraction technique with artificial neural network for classification, a recognition rate of 89.3% is attained. © 2014 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6868361
dc.identifier.doi10.1109/ICCOINS.2014.6868361
dc.identifier.scopus2-s2.0-84938768065
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84938768065&doi=10.1109%2fICCOINS.2014.6868361&partnerID=40&md5=471b25c217fa5baa6d69479fd0552284
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21880
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitle2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings
dc.titleGender recognition on real world faces based on shape representation and neural networken_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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