The Statistical Learning Methods in image processing and Facial Recognition

Basit Öğe Kaydı
dc.contributor.authorMohammed, Tareq Abed
dc.contributor.authorRafeeq, Sarbaz Omar
dc.contributor.authorBayat, Oguz
dc.date.accessioned2025-02-06T18:01:19Z
dc.date.available2025-02-06T18:01:19Z
dc.date.issued2021
dc.departmentAltınbaş Üniversitesien_US
dc.descriptionIARES; IITUen_US
dc.description7th International Conference on Engineering and MIS, ICEMIS 2021 -- 11 October 2021 through 13 October 2021 -- Virtual, Online -- 175544en_US
dc.description.abstractThe aim of this paper is to develop a new approach for The Statistical Learning Methods in image processing and Facial Recognition using the deep learning techniques in python. In the recent years there have been significant advances in face recognition by using deep neural networks. One of the potential next steps is to develop optimized 3D facial recognition. Shifting from 2D to 3D increases complexity of the problem by adding an- other dimension to data, making possible solutions more resource hungry. We will investigate different depth camera based facial recognition techniques and test their performance by deploying them on an embedded processor. We focus on applications for embedded systems and use a small low-resolution time of flight (ToF) camera with our system to keep overall system portable and compact. All faces images are then projected on the feature space ("face space") to find the corresponding coordinators. The face space is composed of "Eigenfaces"or "Fisherfaces"which are actually eigenvectors found after doing a matrix composition - Eigen decomposition. At the heart of Eigenface method is the Principal Component Analysis (PCA) - one of the most popular unsupervised learning algorithms - while Fisherface is a better version of the previous one which makes use of both Principal Component Analysis and Linear Discrimination Analysis (LDA) to get more reliable results. The algorithms were realized by Python in Anaconda. Given initial images in the database, the program can detect and recognize the human faces in the provided pictures before saving them in the database to improve the calculation accuracy in the future. After evaluation, the recognition general results are exported on the screen with details included in the text files. © 2021 ACM.en_US
dc.identifier.doi10.1145/3492547.3492656
dc.identifier.isbn978-145039044-6
dc.identifier.scopus2-s2.0-85122028254
dc.identifier.scopusqualityQ4en_US
dc.identifier.urihttps://doi.org/10.1145/3492547.3492656
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5314
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofACM International Conference Proceeding Seriesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250206
dc.subjectBig dataen_US
dc.subjectFacial Recognitionen_US
dc.subjectLinear Discrimination Analysis (LDA)en_US
dc.subjectLow Computational Poweren_US
dc.subjectMachine Learningen_US
dc.titleThe Statistical Learning Methods in image processing and Facial Recognitionen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu