Face Mask Recognition Menggunakan Model CNN (Convolutional Neural Network) Berbasis Python dan OpenCV
Abstract
During the COVID-19 pandemic, masks are one of the main measuring tools in carrying out health protocols, masks are also a top priority when carrying out activities outside the home or office. Because masks are quite effective in filtering out disease particles that allow users not to get infected. Therefore, many places have made masks an important requirement in maintaining health protocols during the COVID-19 pandemic. Previously there was a system that had been created to assist the government in implementing the mandatory wearing of masks, but there were still deficiencies. Therefore the authors created a system to detect mask wearing by updating previous researchers using the convolutional neural network (CNN) algorithm. For making this system the author uses the PYTHON and OPENCV programming languages. which will produce four parts in this detection, namely Mask, No Mask, Covered Mouth Chin and Covered Nose Mouth.
Downloads
References
A. Dumala, A. Papasani, and S. Vikkurty, ‘COVID-19 Face Mask Live Detection Using OpenCV’, Smart Innovation, Systems and Technologies, vol. 224, pp. 347–352, 2021, doi: 10.1007/978-981-16-1502-3_35/COVER.
H. Adusumalli, D. Kalyani, R. K. Sri, M. Pratapteja, and P. V. R. D. P. Rao, ‘Face Mask Detection Using OpenCV’, in Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, Institute of Electrical and Electronics Engineers Inc., Feb. 2021, pp. 1304–1309. doi: 10.1109/ICICV50876.2021.9388375.
J. Vadlapati, S. Senthil Velan, and E. Varghese, ‘Facial Recognition using the OpenCV Libraries of Python for the Pictures of Human Faces Wearing Face Masks during the COVID-19 Pandemic’, 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, 2021, doi: 10.1109/ICCCNT51525.2021.9579712.
R. Baghel, P. Pahadiya, and U. Singh, ‘Human Face Mask Identification using Deep Learning with OpenCV Techniques’, 7th International Conference on Communication and Electronics Systems, ICCES 2022 - Proceedings, pp. 1051–1057, 2022, doi: 10.1109/ICCES54183.2022.9835884.
G. Harriat Christa, J. Jesica, K. Anisha, and K. M. Sagayam, ‘CNN-based mask detection system using OpenCV and MobileNetV2’, 2021 3rd International Conference on Signal Processing and Communication, ICPSC 2021, pp. 115–119, May 2021, doi: 10.1109/ICSPC51351.2021.9451688.
H. S. Upendra, S. Suman, S. S. Vishnu, and J. Dharani, ‘Real-Time Face Mask Detection using OpenCV and Deep Learning’.
A. Das, M. Wasif Ansari, and R. Basak, ‘Covid-19 Face Mask Detection Using TensorFlow, Keras and OpenCV’, 2020 IEEE 17th India Council International Conference, INDICON 2020, Dec. 2020, doi: 10.1109/INDICON49873.2020.9342585.
K. Suresh, M. Palangappa and S. Bhuvan, "Face Mask Detection by using Optimistic Convolutional Neural Network," 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2021, pp. 1084-1089, doi: 10.1109/ICICT50816.2021.9358653.
S. A. Sanjaya and S. Adi Rakhmawan, "Face Mask Detection Using MobileNetV2 in The Era of COVID-19 Pandemic," 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain, 2020, pp. 1-5, doi: 10.1109/ICDABI51230.2020.9325631.
M. S. Islam, E. Haque Moon, M. A. Shaikat and M. Jahangir Alam, "A Novel Approach to Detect Face Mask using CNN," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 800-806, doi: 10.1109/ICISS49785.2020.9315927.
S. V. Militante and N. V. Dionisio, "Deep Learning Implementation of Facemask and Physical Distancing Detection with Alarm Systems," 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE), Surabaya, Indonesia, 2020, pp. 1-5, doi: 10.1109/ICVEE50212.2020.9243183.
S. Manzoor et al., "Edge Deployment Framework of GuardBot for Optimized Face Mask Recognition With Real-Time Inference Using Deep Learning," in IEEE Access, vol. 10, pp. 77898-77921, 2022, doi: 10.1109/ACCESS.2022.3190538.
M. R. Bhuiyan, S. A. Khushbu and M. S. Islam, "A Deep Learning Based Assistive System to Classify COVID-19 Face Mask for Human Safety with YOLOv3," 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2020, pp. 1-5, doi: 10.1109/ICCCNT49239.2020.9225384.
I. Q. Mundial, M. S. Ul Hassan, M. I. Tiwana, W. S. Qureshi and E. Alanazi, "Towards Facial Recognition Problem in COVID-19 Pandemic," 2020 4rd International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), Medan, Indonesia, 2020, pp. 210-214, doi: 10.1109/ELTICOM50775.2020.9230504.
I. B. Venkateswarlu, J. Kakarla and S. Prakash, "Face mask detection using MobileNet and Global Pooling Block," 2020 IEEE 4th Conference on Information & Communication Technology (CICT), Chennai, India, 2020, pp. 1-5, doi: 10.1109/CICT51604.2020.9312083.
Naufal, Mohammad Farid and Kusuma, Selvia Ferdiana (2021) PENDETEKSI CITRA MASKER WAJAH MENGGUNAKAN CNN DAN TRANSFER LEARNING. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 8 (6). pp. 1293-1300. ISSN 2528-6579
A. Nowrin, S. Afroz, M. S. Rahman, I. Mahmud and Y. -Z. Cho, "Comprehensive Review on Facemask Detection Techniques in the Context of Covid-19," in IEEE Access, vol. 9, pp. 106839-106864, 2021, doi: 10.1109/ACCESS.2021.3100070.
S. MacHiraju, S. Urolagin, R. K. Mishra, and V. Sharma, ‘Face Mask Detection using Keras, Opencv and Tensorflow by Implementing Mobilenetv2’, Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, pp. 1485–1489, 2021, doi: 10.1109/ICAC3N53548.2021.9725546.
Q. Chen and L. Sang, ‘Face-mask recognition for fraud prevention using Gaussian mixture model’, J Vis Commun Image Represent, vol. 55, pp. 795–801, Aug. 2018, doi: 10.1016/J.JVCIR.2018.08.016.
G. Kaur et al., ‘Face mask recognition system using CNN model’, Neuroscience Informatics, vol. 2, no. 3, p. 100035, Sep. 2022, doi: 10.1016/J.NEURI.2021.100035.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Face Mask Recognition Menggunakan Model CNN (Convolutional Neural Network) Berbasis Python dan OpenCV
Pages: 722-730
Copyright (c) 2023 Chuy Mandala Putra, Agung Triayudi, Sari Ningsih

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).