Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8280
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dc.contributor.authorKarahan, Mehmet-
dc.contributor.authorLaçinkaya, F.-
dc.contributor.authorErdönmez, K.-
dc.contributor.authorEminağaoğlu, E.D.-
dc.contributor.authorKasnakoğlu, Coşku-
dc.date.accessioned2022-01-15T13:02:24Z-
dc.date.available2022-01-15T13:02:24Z-
dc.date.issued2022-
dc.identifier.isbn9783030855765-
dc.identifier.issn2367-3370-
dc.identifier.urihttps://doi.org/10.1007/978-3-030-85577-2_24-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8280-
dc.descriptionInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 -- 24 August 2021 through 26 August 2021 -- 264409en_US
dc.description.abstractFace detection is important part of surveillance systems and it has been widely used in computer vision and image processing. Face detection is also first step of the facial feature extraction. Facial feature extraction is a topic that has been focused on by many researchers in computer science, psychology, medicine and related fields and has become increasingly important in recent years. With the help of facial features, machine learning algorithms can estimate ages and classify genders of people. In this paper, face detection, facial feature extraction, age estimation and gender classification are presented. Firstly, face detection and extraction of facial features like eyes, eyebrows, mouth and nose are presented. Secondly, age estimation and gender classification based on the extracted facial features are explained. Experimental results prove that face detection algorithm efficiently detects human faces and facial feature algorithm accurately locates eyes, eyebrows, mouth and nose. Experimental results also show that, based on the extracted facial features, convolutional neural network architecture estimates ages of the people and classifies their gender. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdaBoosten_US
dc.subjectAge classificationen_US
dc.subjectConvolutional neural networken_US
dc.subjectFace detectionen_US
dc.subjectFacial feature extractionen_US
dc.subjectGender classificationen_US
dc.subjectViola-Jones face detectoren_US
dc.titleFace Detection and Facial Feature Extraction with Machine Learningen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.volume308en_US
dc.identifier.startpage205en_US
dc.identifier.endpage213en_US
dc.identifier.scopus2-s2.0-85115255460en_US
dc.institutionauthorKasnakoğlu, Coşku-
dc.identifier.doi10.1007/978-3-030-85577-2_24-
dc.authorscopusid57216759940-
dc.authorscopusid57264307500-
dc.authorscopusid57265432800-
dc.authorscopusid57264307600-
dc.authorscopusid24802064500-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ4-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.5. Department of Electrical and Electronics Engineering-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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