Human Gender Prediction on Facial Images Taken by Mobile Phone using Convolutional Neural Networks

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dc.authoridMohammed Hussein İbrahim İbrahim: 0000-0002-6093-6105
dc.authoridMehmet Hacıbeyoğlu: 0000-0003-1830-8516
dc.contributor.authorHacıbeyoğlu, Mehmet
dc.contributor.authorİbrahim İbrahim, Mohammed Hussein
dc.date.accessioned2020-01-18T21:13:25Z
dc.date.available2020-01-18T21:13:25Z
dc.date.issued2018
dc.departmentNEÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe interest in automatic gender classification has increased rapidly, especially with the growth of online social networkingplatforms, social media applications, and commercial applications. Most of the images shared on these platforms are taken by mobile phonewith different expressions, different angles and low resolution. In recent years, convolutional neural networks have become the mostpowerful method for image classification. Many researchers have shown that convolutional neural networks can achieve better performanceby modifying different network layers of network architecture. Moreover, the selection of the appropriate activation function of neurons,optimizer and the loss function directly affects the performance of the convolutional neural networks. In this study, we propose a genderclassification system from facial images taken by mobile phone using convolutional neural networks. The proposed convolutional neuralnetworks have a simple network architecture with appropriate parameters can be used when rapid training is needed with the amount oflimited training data. In the experimental study, the Adience benchmark dataset was used with 17492 different images with different genderand ages. The classification process was carried out by 10-fold cross validation. According the experimental results, the proposedconvolutional neural networks predicted the gender of the images 98.87% correctly for training and 89.13% for testing.en_US
dc.identifier.citationİbrahim İbrahim, M. H., Hacıbeyoğlu, M. (2018). Human gender prediction on facial images taken by mobile phone using convolutional neural networks. International Journal of Intelligent Systems and Applications in Engineering, 6, 3, 203-208.en_US
dc.identifier.endpage208en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage203en_US
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TXpBM09EWXlNZz09/human-gender-prediction-on-facial-images-taken-by-mobile-phone-using-convolutional-neural-networks
dc.identifier.urihttps://hdl.handle.net/20.500.12452/2978
dc.identifier.volume6en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US]
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional neural networks
dc.subjectDeep learning
dc.subjectFacial mobile images
dc.subjectGender classification
dc.titleHuman Gender Prediction on Facial Images Taken by Mobile Phone using Convolutional Neural Networksen_US
dc.typeArticleen_US

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