Face Mask Detection Using Multi Kernel Support Vector Machine


  • Muhammad Athoillah Universitas PGRI Adi Buana
  • Evita Purnaningrum
  • Rani Kurnia Putri


Coronavirus, Classification, Face Mask, Multi Kernel, Support Vector Machine


Abstract—Coronavirus pandemic has faced humankind for over a year and it looks like it won't be ending anytime soon. Indonesia is one of the countries most affected by this pandemic with millions confirm cases hence the government paly rules to increase strict procedures of using face mask in public area. For this reason, the detection of people wearing a face mask in the public area is needed. Automatically face mask detection is a part of classification problem, thus Support Vector Machine (SVM) can be implemented. This study was aims to build an automatically face mask detector using multi kernel support vector machine. The proposed method was applied by combined various kernels into a one kernel equation. The result presented that the proposed method provided good performances proved by average of value of sensitivity was 83,67, specificity was 82,40%, precision was 82,00%, accuracy was 82,93%, and F-1 Score was 82,77%, better than any other experiment using single kernel SVM tried with same process and dataset.



WHO, “WHO Coronavirus,” 2021. .

KOMINFO, “Data Covid-19 Indonesia,” 2021. .

S. Feng, C. Shen, N. Xia, W. Song, M. Fan, and B. J. Cowling, “Rational use of face masks in the COVID-19 pandemic,” The Lancet Respiratory Medicine. 2020, doi: 10.1016/S2213-2600(20)30134-X.

S. Singh, U. Ahuja, M. Kumar, K. Kumar, and M. Sachdeva, “Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment,” Multimed. Tools Appl., 2021, doi: 10.1007/s11042-021-10711-8.

A. Kumar, A. Kaur, and M. Kumar, “Face detection techniques: a review,” Artif. Intell. Rev., vol. 52, no. 2, pp. 927–948, 2019, doi: 10.1007/s10462-018-9650-2.

X. Jiang, T. Gao, Z. Zhu, and Y. Zhao, “Real-time face mask detection method based on yolov3,” Electron., 2021, doi: 10.3390/electronics10070837.

S. Yadav, “Deep Learning based Safe Social Distancing and Face Mask Detection in Public Areas for COVID-19 Safety Guidelines Adherence,” Int. J. Res. Appl. Sci. Eng. Technol., 2020, doi: 10.22214/ijraset.2020.30560.

M. S. Ejaz, M. R. Islam, M. Sifatullah, and A. Sarker, “Implementation of Principal Component Analysis on Masked and Non-masked Face Recognition,” 2019, doi: 10.1109/ICASERT.2019.8934543.

G. Jignesh Chowdary, N. S. Punn, S. K. Sonbhadra, and S. Agarwal, “Face Mask Detection Using Transfer Learning of InceptionV3,” 2020, doi: 10.1007/978-3-030-66665-1_6.

M. F. Naufal et al., “Comparative Analysis of Image Classification Algorithms for Face Mask Detection,” J. Inf. Syst. Eng. Bus. Intell., 2021, doi: 10.20473/jisebi.7.1.56-66.

M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic,” Meas. J. Int. Meas. Confed., 2021, doi: 10.1016/j.measurement.2020.108288.

A. Oumina, N. El Makhfi, and M. Hamdi, “Control the COVID-19 Pandemic: Face Mask Detection Using Transfer Learning,” 2020, doi: 10.1109/ICECOCS50124.2020.9314511.

M. Ali Khan, “Detection and Classification of Plant Diseases Using Image Processing and Multiclass Support Vector Machine,” Int. J. Comput. Trends Technol., 2020, doi: 10.14445/22312803/ijctt-v68i4p102.

P. Nanglia, S. Kumar, A. N. Mahajan, P. Singh, and D. Rathee, “A hybrid algorithm for lung cancer classification using SVM and Neural Networks,” ICT Express, 2020, doi: 10.1016/j.icte.2020.06.007.

E. Purnaningrum and M. Athoillah, “SVM Approach for Forecasting International Tourism Arrival in East Java,” 2021, doi: 10.1088/1742-6596/1863/1/012060.

A. Patle and D. S. Chouhan, “SVM kernel functions for classification,” in 2013 International Conference on Advances in Technology and Engineering (ICATE), 2013, pp. 1–9, doi: 10.1109/ICAdTE.2013.6524743.

A. Ajam, M. Forghani, M. M. AlyanNezhadi, H. Qazanfari, and Z. Amiri, “Content-based image retrieval using color difference histogram in image textures,” in 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), 2019, pp. 1–6.

Y. N. Nabuasa, “Pengolahan Citra Digital Perbandingan Metode Histogram Equalization Dan Spesification Pada Citra Abu-Abu,” JI Komputer, UN Cendana, C. Digit. E. Histogram, vol. 7, no. 1, pp. 87–95, 2019.

M. Athoillah, I. Irawan, M., and M. Imah, Elly, “Support Vector Machine with Multiple Kernel Learning for Image Retrieval,” in 2015 International Conference on Information, Communication Technology and System, 2015, pp. 17–22.

Y. Zhang and Y. Yang, “Cross-validation for selecting a model selection procedure,” J. Econom., vol. 187, no. 1, pp. 95–112, 2015.

A. Baratloo, M. Hosseini, A. Negida, and G. El Ashal, “Part 1: Simple Definition and Calculation of Accuracy, Sensitivity and Specificity,” Emerg. (Tehran, Iran), vol. 3, no. 2, pp. 48–49, 2015.


Abstract 133  .