Gait-Based Human Gender Classification Using Lifting 5/3 Wavelet and Principal Component Analysis

Basit Öğe Kaydı
dc.contributor.authorHassan, Omer Mohammed Salih
dc.contributor.authorAbdulazeez, Adnan Mohsin
dc.contributor.authorTiryaki, Volkan Möjdat
dc.date.accessioned2024-12-24T19:09:45Z
dc.date.available2024-12-24T19:09:45Z
dc.date.issued2018
dc.departmentSiirt Üniversitesi
dc.description2018 International Conference on Advanced Science and Engineering, ICOASE 2018 -- 9 October 2018 through 11 October 2018 -- Duhok, Kurdistan Region -- 143073
dc.description.abstractThis study describes a representation of gait appearance for the purpose of person identification and classification. The gait representation is based on wavelet 5/3 lifting scheme simple features such as features extracted from video silhouettes of human walking motion. Regardless of its effortlessness, this may lead us to say that, the resulting feature vector contains enough information to perform well on human identification and gender classification tasks. We found out the recognition behaviors of different methods to total features over time functions under different recognition tasks. In addition to that, we provide results of gender classification based on our gait appearance features using a (C4.5 algorithm). So, the result of classification rate for CASIA-B gait databases is 97.98% and the result of recognition rate for OU-ISIR gait Database Large Population Dataset is 97.5%, these results have been obtained from gender classification data. Gait database demonstrates that the proposed method achieves better recognition performance than the most existing methods in the literature, and particularly under certain walking variations. © 2018 IEEE.
dc.identifier.doi10.1109/ICOASE.2018.8548909
dc.identifier.endpage178
dc.identifier.isbn978-153866696-8
dc.identifier.scopus2-s2.0-85060042289
dc.identifier.scopusqualityN/A
dc.identifier.startpage173
dc.identifier.urihttps://doi.org10.1109/ICOASE.2018.8548909
dc.identifier.urihttps://hdl.handle.net/20.500.12604/3741
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofICOASE 2018 - International Conference on Advanced Science and Engineering
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectC4.5 Algorithm
dc.subjectGait Recognition
dc.subjectLifting 5/3
dc.subjectPrincipal Component Analysis (PCA)
dc.titleGait-Based Human Gender Classification Using Lifting 5/3 Wavelet and Principal Component Analysis
dc.typeConference Object

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu