Mask Detection From Face Images Using Deep Learning and Transfer Learning
| dc.contributor.author | Örnek, Ahmet Haydar | |
| dc.contributor.author | Çelik, Mustafa | |
| dc.contributor.author | Ceylan, Murat | |
| dc.date.accessioned | 2022-05-23T20:07:31Z | |
| dc.date.available | 2022-05-23T20:07:31Z | |
| dc.date.issued | 2021 | |
| dc.description | 15th Turkish National Software Engineering Symposium, UYMS 2021 -- 17 November 2021 through 19 November 2021 -- -- 176220 | en_US |
| dc.description.abstract | It is vital important for people to wear masks during the pandemic that affects the whole world. In this study, it was detected whether people wear masks by using convolutional neural networks which is one of the deep learning methods and transfer learning. In the classification carried out using the Resnet-18 architecture, both real-life images obtained with the Huawei M2150 camera and images shared as open source were used. The system, which was trained using 18600 images, was tested with 4540 real-world images and 95.16% sensitivity 96.69% specificity values were obtained. Thus, a model that works with high performance not only on high resolution images taken close up, but also on low resolution images taken from afar was obtained. © 2021 IEEE. | en_US |
| dc.identifier.doi | 10.1109/UYMS54260.2021.9659582 | |
| dc.identifier.isbn | 9781665410700 | |
| dc.identifier.scopus | 2-s2.0-85124797403 | |
| dc.identifier.uri | https://doi.org/10.1109/UYMS54260.2021.9659582 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.13091/2381 | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 2021 Turkish National Software Engineering Symposium, UYMS 2021 - Proceedings | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | mask detection | en_US |
| dc.subject | transfer learning | en_US |
| dc.subject | Convolutional neural networks | en_US |
| dc.subject | Face recognition | en_US |
| dc.subject | Transfer learning | en_US |
| dc.subject | Wear of materials | en_US |
| dc.subject | Convolutional neural network | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Face images | en_US |
| dc.subject | Learning methods | en_US |
| dc.subject | Learning Transfer | en_US |
| dc.subject | Mask detection | en_US |
| dc.subject | Open-source | en_US |
| dc.subject | Real-life images | en_US |
| dc.subject | Real-world image | en_US |
| dc.subject | Transfer learning | en_US |
| dc.subject | Deep learning | en_US |
| dc.title | Mask Detection From Face Images Using Deep Learning and Transfer Learning | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
| gdc.author.scopusid | 57210593918 | |
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| gdc.coar.access | metadata only access | |
| gdc.coar.type | text::conference output | |
| gdc.description.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
| gdc.description.endpage | 4 | |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | N/A | |
| gdc.description.startpage | 1 | |
| gdc.description.wosquality | N/A | |
| gdc.identifier.openalex | W4206156330 | |
| gdc.identifier.wos | WOS:000813101100022 | |
| gdc.index.type | WoS | |
| gdc.index.type | Scopus | |
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| gdc.openalex.collaboration | National | |
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| gdc.opencitations.count | 2 | |
| gdc.plumx.crossrefcites | 2 | |
| gdc.plumx.mendeley | 3 | |
| gdc.plumx.scopuscites | 4 | |
| gdc.scopus.citedcount | 4 | |
| gdc.virtual.author | Ceylan, Murat | |
| gdc.wos.citedcount | 0 | |
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