Modified Multi-Kernel Support Vector Machine for Mask Detection
DOI:
https://doi.org/10.21512/commit.v16i2.7873Keywords:
Modified Multi-Kernel, Support Vector Machine (SVM), Mask DetectionAbstract
Indonesia is one of the countries most affected by the Coronavirus pandemic with millions confirm cases. Hence, the government has increased strict procedures for using face masks in public areas. For this reason, the detection of people wearing face masks in public areas is needed. Face mask detection is a part of the classification problem. Thus Support Vector Machine (SVM) can be implemented. SVM is still known as one of the most powerful and efficient classification algorithms. The research aims to build an automatic face mask detector using SVM. However, it needs to modify it first because it only can classify linear data. The modification is made by adding kernel functions, and a Multi-kernel approach is chosen. The proposed method is applied by combining various kernels into one kernel equation. The dataset used in the research is a face mask image obtained from Github. The data are public datasets consisting of faces with and without masks. The results present that the proposed method provides good performance. It is proven by the average value. The values are 83.67% for sensitivity, 82.40% for specificity, 82.00% for precision, 82.93% for accuracy, and 82.77% for F1-score. These values are better than other experiments using single kernel SVM with the same process and dataset.
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References
WHO, “WHO Coronavirus (COVID-19) dashboard,” 2022. [Online]. Available: https: //covid19.who.int/table
Kementerian Kesehatan Republik Indonesia, “Vaksinasi COVID-19 nasional,” 2022. [Online]. Available: https://vaksin.kemkes.go.id/#/vaccine
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, vol. 8, no. 5, pp. 434–436, 2020.
S. Singh, U. Ahuja, M. Kumar, K. Kumar, and M. Sachdeva, “Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment,” Multimedia Tools and Applications, vol. 80, no. 13, pp. 19 753–19 768, 2021.
A. Kumar, A. Kaur, and M. Kumar, “Face detection techniques: A review,” Artificial Intelligence Review, vol. 52, no. 2, pp. 927–948, 2019.
P. Nagrath, R. Jain, A. Madan, R. Arora, P. Kataria, and J. Hemanth, “SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2,” Sustainable Cities and Society, vol. 66, pp. 1–11, 2021.
S. Sethi, M. Kathuria, and T. Kaushik, “Face mask detection using deep learning: An approach to reduce risk of coronavirus spread,” Journal of biomedical informatics, vol. 120, pp. 1–12, 2021.
T. Meenpal, A. Balakrishnan, and A. Verma, “Facial mask detection using semantic segmentation,” in 2019 4th International Conference on Computing, Communications and Security (ICCCS). Rome, Italy: IEEE, Oct. 10–12, 2019, pp. 1–5.
X. Jiang, T. Gao, Z. Zhu, and Y. Zhao, “Real-time face mask detection method based on YOLOv3,” Electronics, vol. 10, no. 7, pp. 1–17, 2021.
S. Yadav, “Deep learning based safe social distancing and face mask detection in public areas for COVID-19 safety guidelines adherence,” International Journal for Research in Applied Science and Engineering Technology, vol. 8, no. 7, pp. 1368–1375, 2020.
M. S. Ejaz, M. R. Islam, M. Sifatullah, and A. Sarker, “Implementation of principal component analysis on masked and non-masked face recognition,” in 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT). Dhaka, Bangladesh: IEEE, May 3–5, 2019, pp. 1–5.
G. Jignesh Chowdary, N. S. Punn, S. K. Sonbhadra, and S. Agarwal, “Face mask detection using transfer learning of inceptionv3,” in International Conference on Big Data Analytics. Sonepat, India: Springer, Dec. 15–18, 2020, pp. 81–90.
M. F. Naufal, S. F. Kusuma, Z. A. Prayuska, A. A. Yoshua, Y. A. Lauwoto, N. S. Dinata, and D. Sugiarto, “Comparative analysis of image classification algorithms for face mask detection,” Journal of Information Systems Engineering and Business Intelligence, vol. 7, no. 1, pp. 56–66, 2021.
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,” Measurement, vol. 167, pp. 1–11, 2021.
A. Oumina, N. El Makhfi, and M. Hamdi, “Control the covid-19 pandemic: Face mask detection using transfer learning,” in 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). Kenitra, Morocco: IEEE, Dec. 2–3, 2020, pp. 1–5.
J. Cervantes, F. Garcia-Lamont, L. Rodr´ıguez Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189–215, 2020.
M. A. Chandra and S. S. Bedi, “Survey on SVM and their application in image classification,” International Journal of Information Technology, vol. 13, no. 5, pp. 1–11, 2021.
H. F. Kareem, M. S. Al-Huseiny, F. Y. Mohsen, and K. Al-Yasriy, “Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 3, pp. 1731–1738, 2021.
S. Saeed, J. Baber, M. Bakhtyar, I. Ullah, N. Sheikh, I. Dad, and A. A. Sanjrani, “Empirical evaluation of SVM for facial expression recognition,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 11, pp. 670–673, 2018.
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). Shahrood, Iran: IEEE, Dec. 18–19, 2019, pp. 1–6.
Y. N. Nabuasa, “Pengolahan citra digital perbandingan metode histogram equalization dan spesification pada citra abu-abu,” J-Icon: Jurnal Komputer dan Informatika, vol. 7, no. 1, pp. 87–95, 2019.
M. A. Khan, “Detection and classification of plant diseases using image processing and multiclass support vector machine,” International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 5–11, 2020.
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, vol. 7, no. 3, pp. 335–341, 2021.
E. Purnaningrum and M. Athoillah, “SVM approach for forecasting international tourism arrival in East Java,” Journal of Physics: Conference Series, vol. 1863, no. 1, pp. 1–6, 2021.
A. Patle and D. S. Chouhan, “SVM kernel functions for classification,” in 2013 International Conference on Advances in Technology and Engineering (ICATE). Mumbai, India: IEEE, Jan. 23–25, 2013, pp. 1–9.
M. G¨onen and E. Alpaydın, “Multiple kernel learning algorithms,” Journal of Machine Learning Research, vol. 12, pp. 2211–2268, 2011.
A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet, “SimpleMKL,” Journal of Machine Learning Research, vol. 9, pp. 2491–2521, 2008.
Y. Zhang and Y. Yang, “Cross-validation for selecting a model selection procedure,” Journal of Econometrics, 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,” Emergency, vol. 3, no. 2, pp. 48–49, 2015.
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