| Yazarlar (9) |
Emrah Aydemir
Sakarya Üniversitesi, Türkiye |
Dr. Öğr. Üyesi Mehmet Ali YALÇINKAYA
Kırşehir Ahi Evran Üniversitesi, Türkiye |
|
Prabal Datta Barua
|
|
Mehmet Bayğın
Türkiye |
|
Oliver Faust
|
|
Şengül Doğan
Fırat Üniversitesi, Türkiye |
|
Subrata Chakraborty
|
|
Türker Tuncer
Fırat Üniversitesi, Türkiye |
|
Rajendra Acharya Udyavara
|
| Özet |
| Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time. |
| Anahtar Kelimeler |
| face mask detection | ResNet101 | DenseNet201 | transfer learning | hybrid feature selector | support vector machine |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | International Journal of Environmental Research and Public Health |
| Dergi ISSN | 1660-4601 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2022 |
| Cilt No | 19 |
| Sayı | 4 |
| Doi Numarası | 10.3390/ijerph19041939 |
| Makale Linki | https://www.mdpi.com/1660-4601/19/4/1939 |
| Atıf Sayıları | |
| WoS | 14 |
| SCOPUS | 18 |
| Google Scholar | 20 |
