Identification of Medical Mask Use by Applying the Convolutional Neural Network Algorithm and the Gabor Filter with Multiclass Classification




convolutional neural network, gabor filter, medical mask classification, multiclass classification


Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) causes global pandemics and makes countries around the world lock down fortourists. This action is required to prevent the spread of viruses that take 14 days to disappear. SARS-COV-2 can easily infect individuals through a droplet. Thus, the governments of every country worldwide recommend wearing medical masks to prevent the spread of viruses, as well as maintaining distance during activities with others and washing hands frequently. Medical masks become efficient if their application is precise, owing to a lack of knowledge and self-awareness to preserve their distance and wash their hands. This paper proposes a Convolutional Neural Network (CNN) with Gabor filter implementation. The simulation uses a mask on a dataset with over 70,000 individual photos. The results demonstrated that the proposed CNN-Gabor model in this work could effectively classify the position of the mask when compared to the CNN model without the Gabor filter.

Author Biography

Erik Iman Heri Ujianto, University of Technology Yogyakarta, Indonesia

University of Technology Yogyakarta: Master of Information Technology Yogyakarta, Indonesia


Subhamastan Rao, T., Anjali Devi, S., Dileep, P., & Sitha Ram, M. (2020). A Novel Approach to Detect Face Mask to Control Covid Using Deep Learning. European Journal of Molecular and Clinical Medicine, 7(6), 658–668.

Isbaniah, F. (2020). Pedoman Pencegahan dan Pengendalian Coronavirus Disease (COVID-19). In Germas. 04_Pedoman_P2_COVID-19 27_Maret2020_TTD1.pdf

Mohammed, M. (2016). Machine learning: Algorithms and Applications.

Wiranda, N., Purba, H. S., & Sukmawati, R. A. (2020). Survei Penggunaan Tensorflow pada Machine Learning untuk Identifikasi Ikan Kawasan Lahan Basah. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 10(2), 179.

Evolve, M. L. (2019). Mengenal Machine Learning. Medium.Com. Fukushima, K., & Miyake, S. (1982). Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition (pp. 267–285).

Ejaz, Md. S., & Islam, Md. R. (2019). Masked Face Recognition Using Convolutional Neural Network. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), 1–6.

Venkateswarlu, I. B., Kakarla, J., & Prakash, S. (2020). Face mask detection using MobileNet and Global Pooling Block. 2020 IEEE 4th Conference on Information & Communication Technology (CICT), 1–5.

Xu, M., Wang, H., Yang, S., & Li, R. (2020). Mask wearing detection method based on SSD-Mask algorithm. 2020 International Conference on Computer Science and Management Technology (ICCSMT), 138–143.

Vinh, T. Q., & Anh, N. T. N. (2020). Real-Time Face Mask Detector Using YOLOv3 Algorithm and Haar Cascade Classifier. 2020 International Conference on Advanced Computing and Applications (ACOMP), 146–149.

Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2021a). A hybrid deep transfer learning model with machine learning methods forface mask detection in the era of the COVID-19 pandemic. Measurement, 167, 108288.

Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2021b). Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection. Sustainable Cities and Society, 65, 102600.

Srinivasan, S., Rujula Singh, R., Biradar, R. R., & Revathi, S. (2021). COVID-19 Monitoring System using Social Distancing and Face Mask Detection on Surveillance video datasets. 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), 449–455.

Sethi, S., Kathuria, M., Kaushik T. (2021). Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread, Journalof Biomedical Informatics, Volume 120, 2021.

Gabor. (1946). Theory of Communication. Institution of Electrical Engineering, 93(3), 429–457.

Jing Yi, Yong, & Phooi. (2007). Gabor Filters and Grey-level Cooccurrence Matrices in Texture Classification. MMU International Symp. on Information and Communications Technologies.

Hammoud. (2000). Texture segmentation using Gabor filters. KES’2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516), 109–




How to Cite

Muh. Arifandi, & Heri Ujianto, E. I. . (2023). Identification of Medical Mask Use by Applying the Convolutional Neural Network Algorithm and the Gabor Filter with Multiclass Classification. International Journal of Computer and Information Technology(2279-0764), 12(3).