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dc.contributor.authorKutlugün, Mehmet Ali
dc.contributor.authorŞirin, Yahya
dc.contributor.authorKarakaya, M.
dc.date.accessioned2020-12-20T06:50:05Z
dc.date.available2020-12-20T06:50:05Z
dc.date.issued2019
dc.identifier.isbn9788395541605
dc.identifier.urihttps://doi.org/10.15439/2019F181
dc.identifier.urihttps://hdl.handle.net/20.500.12436/1913
dc.descriptionIntelen_US
dc.description2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019 -- 1 September 2019 through 4 September 2019 -- -- 152545en_US
dc.description.abstractNowadays, deep learning methods have been used in many areas such as big data analysis, speech and image processing with the increasing processing power and the development of graphics processors. In particular, face recognition systems have become one of the most important research topics in biometry. Light direction, reflection, emotional and physical changes in facial expression are the main factors in face recognition systems that make recognition difficult. Training of the system with the available data in small data sets is an important factor that negatively affects the performance. The Convolutional Neural Network (CNN) model is a deep learning architecture used for large amounts of training data. In this study, a small number of employee images set of a small-scale company has been increased by applying different filters. In addition, it has been tried to determine which data augmentation options have more effect on face recognition. Thus, non-real-time face recognition has been performed by training with new augmented dataset of each picture with many features. © 2019 Polish Information Processing Society - as since.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019en_US
dc.identifier.doi10.15439/2019F181en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectData augmentationen_US
dc.subjectDeep learningen_US
dc.subjectFace recognitionen_US
dc.subjectImage processingen_US
dc.titleThe effects of augmented training dataset on performance of convolutional neural networks in face recognition systemen_US
dc.typeconferenceObjecten_US
dc.authoridMehmet Ali Kutlugün |0000-0003-0720-2142
dc.departmentMühendislik ve Doğa Bilimleri Fakültesien_US
dc.identifier.startpage929en_US
dc.identifier.endpage932en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.institutionauthorKutlugün, Mehmet Ali
dc.institutionauthorŞirin, Yahya
dc.department-tempKutlugun, M.A., Computer Science and Engineering, Istanbul Sabahattin Zaim University, Istanbul, Turkey; Sirin, Y., Computer Science and Engineering, Istanbul Sabahattin Zaim University, Istanbul, Turkey; Karakaya, M., Management Information Systems, Anadolu University, Eskişehir, Turkeyen_US


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