New approaches based on local binary patterns for gender identification from sensor signals

Basit Öğe Kaydı
dc.authoridKUNCAN, Melih/0000-0002-9749-0418
dc.contributor.authorKuncan, Fatma
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorKuncan, Melih
dc.date.accessioned2024-12-24T19:30:24Z
dc.date.available2024-12-24T19:30:24Z
dc.date.issued2019
dc.departmentSiirt Üniversitesi
dc.description.abstractGender identification (GI) is to determine the sex of the individual based on the characteristics that distinguish between male and female. In this study, three different feature extraction methods are proposed for gender identification by using signals obtained from accelerometers, magnetometers and gyroscope sensors installed in 5 different body parts of the individuals. Feature extraction from signals is one of the most critical stages of GI. Because the success of GI depends on the features, different transformation methods have been applied to the signals obtained from sensors such as One Dimensional Local Binary Patterns (1D-LBPs), One Dimensional Robust Local Binary Patterns (1D-RLBPs) and Weighted One Dimensional Robust Local Binary Patterns (W-1D- RLBPs). By using these features, different machine learning methods (SVM, RF, ANN, Knn) were elaborated for classification. According to the results, it was seen that 1D-LBPs, 1D-RLBPs and W-1D-RLBPs methods provided useful features for GI. 96.04%, 96.72% and 97.28% success rates were perceived with the proposed methods, respectively. In the study, features, motions, sensor port, sensor type that affect GI success were determined. The achievements of the proposed methods have been found to be more successful than the feature groups provided by the frequency and time domains of the proposed feature extraction methods, which are also compared with the success of the feature groups derived from the same sensor signals from the time and frequency domains.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Siirt University [2018-SIUFEB-DR-009]
dc.description.sponsorshipThis work was supported by Scientific Research Projects Coordination Unit of Siirt University as a project with the number 2018-SIUFEB-DR-009.
dc.identifier.doi10.17341/gazimmfd.426259
dc.identifier.endpage2185
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85069883078
dc.identifier.scopusqualityQ2
dc.identifier.startpage2173
dc.identifier.trdizinid389844
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.426259
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/389844
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7521
dc.identifier.volume34
dc.identifier.wosWOS:000486923100010
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isotr
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_20241222
dc.subjectGender identification
dc.subjectsensor signals
dc.subjectone dimensional local binary patterns
dc.subjectone dimensional robust local binary patterns
dc.subjectweighted one-dimensional robust local binary Patterns
dc.subjectfeature extraction
dc.titleNew approaches based on local binary patterns for gender identification from sensor signals
dc.title.alternativeSensör işaretlerinden cinsiyet tanıma için yerel ikili örüntüler tabanlı yeni yaklaşımlar
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu