A hybrid approach based on deep learning for gender recognition using human ear images

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dc.authoridKarasulu, Bahadir/0000-0001-8524-874X
dc.contributor.authorKarasulu, Bahadir
dc.contributor.authorYucalar, Fatih
dc.contributor.authorBorandag, Emin
dc.date.accessioned2025-01-27T20:29:47Z
dc.date.available2025-01-27T20:29:47Z
dc.date.issued2022
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractNowadays, the use of the human ear images gains importance for the sustainability of biometric authorization and surveillance systems. Contemporary studies show that such processes can be done semi-automatically or fully automatically, instead of being done manually. Due to the fact that deep learning uses abstract features (i.e., representation learning), it reaches quite high performance values compared to classical methods. In our study, a synergistic gender recognition approach based on hybrid deep learning was created based on the use of human ear images in classifying people fully automatically according to their gender. By means of hybridization, hybrid deep neural network architectural models are used, which include both convolutional neural network component and recurrent neural network type components together. In these models, long-short term memory and gated recurrent unit are taken as recurrent neural network type components. Thanks to these components, the hybrid model extracts the relational dependencies between the pixel regions in the image very well. On account of this synergistic approach, the gender classification accuracy of hybrid models is higher than the standalone convolutional neural network model in our study. Two different image datasets with gender marking were used in our experiments. The reliability of the experimental results has been proven by objective metrics. In the conducted experiments, the highest values in gender recognition with hybrid models were obtained with the test accuracy of 85.16% for the EarVN dataset and 87.61% for the WPUT dataset, respectively. Discussion and conclusions are included in the last section of our study.
dc.identifier.doi10.17341/gazimmfd.945188
dc.identifier.endpage1594
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85128775906
dc.identifier.scopusqualityQ2
dc.identifier.startpage1579
dc.identifier.trdizinid508626
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.945188
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/508626
dc.identifier.urihttps://hdl.handle.net/20.500.12428/23038
dc.identifier.volume37
dc.identifier.wosWOS:000834843300031
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherGazi Univ, Fac Engineering Architecture
dc.relation.ispartofJournal of The Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectHuman Ear
dc.subjectGender Recognition
dc.subjectDeep Learning
dc.subjectConvolutional Neural Network
dc.subjectRecurrent Neural Network
dc.titleA hybrid approach based on deep learning for gender recognition using human ear images
dc.title.alternativeInsan kula?i görüntüleri kullanarak cinsiyet tanima için derin ö?renme tabanli melez bir yaklaşim
dc.typeArticle

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