DETEKSI MASKER WAJAH DENGAN PYTHON OPENCV MENGGUNAKAN METODE HAARCASCADE
DOI:
https://doi.org/10.31884/random.v4i1.43Keywords:
Debu dan Virus, Masker, Penyakit Pernafasan, HaarcascadeAbstract
Dust and viruses are reasons for someone to wear a mask. Because it can be the cause of diseases that attack the respiratory system. This condition is very important and needs to be watched out for. For this reason, someone uses a mask to protect himself, because wearing a mask is one way to reduce the risk of respiratory diseases. This makes the use of masks make digital image processing have good benefits, namely to detect the use of masks. With the existence of research on this system can find out the face of someone who uses a mask or not. The system was created using the Python and OpenCV programming languages with the Haarcascade method which is able to identify wearing medical masks, wearing non-medical masks, wearing masks incorrectly and people who are not wearing masks. Images or pictures and videos captured via a webcam, computer or laptop camera
Downloads
References
WHO. (2018). Air pollution and child health: prescribing clean air. World Health Organization.
Leung, N. H. L., et al. (2020). Respiratory virus shedding in exhaled breath and efficacy of face masks. Nature Medicine, 26(5), 676–680.
MacIntyre, C. R., & Chughtai, A. A. (2020). A rapid systematic review of the efficacy of face masks and respirators against coronaviruses and other respiratory transmissible viruses for the community, healthcare workers and sick patients. International Journal of Nursing Studies, 108.
Pandian, S. R., et al. (2021). Face mask detection using deep learning: A review. Journal of Intelligent & Fuzzy Systems, 40(5), 8771–8781.
Loey, M., Manogaran, G., & Taha, M. H. N. (2021). 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.
Athif, M., Wibowo, F. W., Huda, M. H., & Firdaus, F. (2023). Face Mask Detection under Varying Lighting Conditions Using Convolutional Neural Network. International Journal of Computer Applications, 182(23), 15–20.
Pramono, H. A., & Haryanto, H. (2022). Implementation of CNN for Face Mask Detection in Public Spaces: A Comparison with YOLO-v4. Proceedings of the International Conference on Advanced Computer and Information Systems (ICACIS), 78–84.
Rajaraman, S., et al. (2020). Face mask detection using MobileNetV2 in TensorFlow. arXiv preprint arXiv:2008.11117.
Ahmad, M., et al. (2021). Real-time face mask detection using OpenCV and Haarcascade classifier. International Journal of Advanced Computer Science and Applications, 12(5), 321–328.
Nasir, M. U., et al. (2022). A robust face mask detection system using Haarcascade and LBPH. Journal of Computer Applications, 44(1), 45–50.
Singh, A., et al. (2021). AI-based mask detection and monitoring in public places using surveillance systems. International Journal of Emerging Technologies in Learning, 16(5), 210–217.
Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Ahmad, M., Rehman, A., & Hussain, M. (2021). Real-time face mask detection using OpenCV and Haarcascade classifier. International Journal of Advanced Computer Science and Applications (IJACSA), 12(5), 321–328. https://doi.org/10.14569/IJACSA.2021.0120539
Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 511–518. https://doi.org/10.1109/CVPR.2001.990517
Athif, M., Wibowo, F. W., Huda, M. H., & Firdaus, F. (2023). Face mask detection under varying lighting conditions using convolutional neural network. International Journal of Computer Applications, 182(23), 15–20.
Upendra, K., & Pujari, H. (2021). Real-Time Face Mask Detection using OpenCV and Deep Learning. CEUR Workshop Proceedings, 2849, 85–89.
Sari, A. I., & Nugroho, H. A. (2023). Comparative Study of CNN Architectures for Face Mask Detection in Complex Environments. International Research Journal on Advanced Science Hub, 5(3), 64–72.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Informatics and Computing
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.