Face Mask Detection Using GoogLeNet CNN-Based SVM Classifiers

Yıl 2023, Cilt: 36 Sayı: 2, 645 - 658, 01.06.2023

Kubilay Muhammed Sünnetci , Selahaddin Batuhan Akben , Mevlüde Merve Kara , Ahmet Alkan

https://doi.org/10.35378/gujs.1009359

Cited By: 2

Öz

The COVID-19 pandemic that broke out in 2019 has affected the whole world, and in late 2021 the number of cases is still increasing rapidly. In addition, due to this pandemic, all people must follow the mask and cleaning rules. Herein, it is now mandatory to wear a mask in places where millions of people working in many workplaces work. Hence, artificial intelligence-based systems that can detect face masks are becoming very popular today. In this study, a system that can automatically detect whether people are masked or not is proposed. Here, we extract image features from each image using the GoogLeNet architecture. With the help of these image features, we train GoogLeNet based Linear Support Vector Machine (SVM), Quadratic SVM, and Coarse Gaussian SVM classifiers. The results show that the accuracy (%), sensitivity (%), specificity (%) precision (%), F1 score (%), and Matthews Correlation Coefficient (MCC) values of GoogLeNet based Linear SVM is equal to 99.55-99.55-99.55-99.55-99.55-0.9909. When the results of the proposed system are examined, it is seen that it provides an advantage due to its high accuracy. In addition, it is very useful in practice that it can detect masks from any camera. Moreover, since there are classification models that can be created in a shorter time than models that can detect objects, model results can be examined in a shorter time. Therefore, it is seen that the proposed system also provides an advantage in terms of complexity.

Anahtar Kelimeler

Face mask detection, Deep learning, Machine learning, GoogLeNet, SVM

Kaynakça

• [1] Worldometer Coronavirus Cases, www.worldometers.info. Access date: 5.11.2020.
• [2] Howard, J., Huang, A., Li Z., Tufekci, Z., Zdimal V., Van der Westhuizen, H.M., Von Delft, A., Price, A., Fridman, L., Tang, L.H., Tang, V., Watson, G.L., Bax, C.E., Shaikh, R., Questier, F., Hernandez, D., Chu, L.F., Ramirez, C.M., Rimoin, A.W., “An evidence review of face masks against COVID-19”, Proceedings of the National Academy of Sciences of the United States of America, 118(4): 1–12, (2021).
• [3] Kurlekar, S., A. Omanna, A., Deshpande, O.A., Dinesh, B., “Face Mask Detection System Using Deep Learning”, Turkish Journal of Computer and Mathematics Education, 12(7): 1327–1332, (2021).
• [4] Reddy, P.S., Nandini, M., Mamatha, E., Reddy, K.V., Vishant A., “Face Mask Detection using Machine Learning Techniques”, International Conference on Trends in Electronics and Informatics (ICEI), IEEE, 1468–1472, (2021).
• [5] Srivathsa, K., Rengarajan, A., Kumar, N., “Detecting of Face Mask”, International Research Journal of Modernization in Engineering Technology and Science, 12: 2582–5208, (2020).
• [6] Siegfried, I.M., “Comparative Study of Deep Learning Methods in Detection Face Mask Utilization”, PrePrint, 1–7, (2020).
• [7] Bhadani, A.K., Sinha A., “A Facemask Detector using Machine Learning and Image Processing Techniques”, Engineering Science and Technology, an International Journal, 0–8, (2020).
• [8] Fan, X., Jiang, M., Yan, H., “A Deep Learning Based Light-Weight Face Mask Detector with Residual Context Attention and Gaussian Heatmap to Fight against COVID-19”, IEEE Access, 9: 96964–96974, (2021).
• [9] Nowrin, A., Afroz, S., Rahman, M.S., Mahmud, I., Cho, Y.Z., “Comprehensive Review on Facemask Detection Techniques in the Context of Covid-19”, IEEE Access, 9: 106839–106864, (2021).
• [10] Loey, M., Manogaran, G., Taha, M.H.N., Khalifa, N.E.M., “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, (2021).
• [11] Snyder, S.E., Husari, G., “Thor: A deep learning approach for face mask detection to prevent the COVID-19 pandemic”, IEEE SOUTHEASTCON, (2021).
• [12] Nagrath, P., Jain, R., Madan, A., Arora, R., Kataria, P., Hemanth, J., “SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2”, Sustainable Cities and Society, 66: 102692, (2021).
• [13] Vijitkunsawat, W., Chantngarm, P., “Study of the Performance of Machine Learning Algorithms for Face Mask Detection”, International Conference on Information Technology (INCIT), 1: 39–43, (2020).
• [14] Balaji, S., Balamurugan, B., Kumar, T.A.,R. Rajmohan, Kumar, P.P., “A brief Survey on AI Based Face Mask Detection System for Public Places” Irish Interdisciplinary Journal of Science & Research, 5(1): 108-117, (2021).
• [15] Bhuiyan, M.R., Khushbu, S.A., Islam, M.S., “A Deep Learning Based Assistive System to Classify COVID-19 Face Mask for Human Safety with YOLOv3”, International Conference on Computing and Networking Technology (ICCNT), (2020).
• [16] Nagoriya, H., Parekh, M., “Live Facemask Detection System”, International Journal of Imaging and Robotics, 21(1): 1–8, (2021).
• [17] Militante, S.V., Dionisio, N.V., “Deep Learning Implementation of Facemask and Physical Distancing Detection with Alarm Systems”, Third International Conference on Vocational Education and Electrical Engineering (ICVEE), (2020).
• [18] Basha, C.Z., Pravallika, B.N.L., Shankar, E.B., “An efficient face mask detector with pytorch and deep learning”, EAI Endorsed Transactions on Pervasive Health and Technology, 7(25): 1–8, (2021).
• [19] Mao, P., Hao, P., Xin, Y., “Deep Learning Implementation of Facemask Detection”, In The 2nd International Conference on Computing and Data Science, 16898, (2021).
• [20] Arora, R., Dhingra, J., Sharma, A., “Face Mask Detection using Machine Learning and Deep Learning”, International Research Journal of Engineering and Technology, 8(1): (2021).
• [21] Suresh, K., Palangappa, M.B., Bhuvan, S., “Face Mask Detection by using Optimistic Convolutional Neural Network”, International Conference on Inventive Computation Technologies (ICICT), 1084–1089, (2021).
• [22] Sethi, S., Kathuria, M., Kaushik, T., “Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread”, Journal of Biomedical Informatics, 120: 103848, (2021).
• [23] Inamdar, M., Mehendale, N., “Real-Time Face Mask Identification Using Facemasknet Deep Learning Network”, SSRN Electronic Journal, (2020).
• [24] Asif, S., Wenhui, Y., Tao, Y., Jinhai, S., Amjad, K., “Real Time Face Mask Detection System using Transfer Learning with Machine Learning Method in the Era of Covid-19 Pandemic”, International Conference on Artificial Intelligence and Big Data (ICAIBD), 70–75, (2021).
• [25] Loey, M., Manogaran, G., Taha, M.H.N., Khalifa, N.E.M., “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic”, Measurement, 167: 108288, (2021).
• [26] Mohan, P., Paul, A.J., Chirania, A., “A tiny cnn architecture for medical face mask detection for resource-constrained endpoints”, Innovations in Electrical and Electronic Engineering, 657–670, (2021).
• [27] Reza, S.R., Dong, X., Qian, L., “Robust Face Mask Detection using Deep Learning on IoT Devices”, 2021 IEEE International Conference on Communications Workshops (ICC Workshops), (2021).
• [28] Mbunge, E., Simelane, S., Fashoto, S.G., Akinnuwesi, B., Metfula, A.S., “Application of deep learning and machine learning models to detect COVID-19 face masks - A review”, Sustainable Operations and Computers, 2: 235–245, (2021).
• [29] Tuncer, S.A., Alkan, A., “A decision support system for detection of the renal cell cancer in the kidney”, Measurement, 123: 298–303, (2018).
• [30] Güney, S., Atasoy, A., “Multiclass classification of n-butanol concentrations with k-nearest neighbor algorithm and support vector machine in an electronic nose”, Sensors and Actuators B: Chemical, 166: 721–725, (2012).
• [31] Alkan, A., Günay, M., “Identification of EMG signals using discriminant analysis and SVM classifier”, Expert systems with Applications, 39(1): 44–47, (2012).
• [32] Akben, S.B., “Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis, Blood Analysis and Disease History”, Irbm, 39(5): 353–358, (2018).
• [33] Akben, S.B., Alkan, A., “Visual interpretation of biomedical time series using Parzen window-based density-amplitude domain transformation”, PLoS One, 11(9): 1–13, (2016).
• [34] Kaggle Face Mask Detection, https://www.kaggle.com/dhruvmak/face-mask-detection. Access date: 5 November 2020.
• [35] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., “Going deeper with convolutions”, Proceedings of the IEEE conference on computer vision and pattern recognition, 1–9, (2015).
• [36] Matlab Googlenet, www.mathworks.com. Access date: 5 November 2020.
• [37] Jakkula, V., “Tutorial on Support Vector Machine (SVM)”, School of EECS, Washington State University, 1–13, (2011).
• [38] Sünnetci, K.M., Ordu, M., Alkan, A., “Gait based human identification a comparative analysis”, Computer Science (Special), 116-125, (2021).
• [39] Göksu, M., Sünnetci, K.M., Alkan, A., “Derin öğrenme ağları kullanılarak mısır yapraklarında hastalık tespiti”, Computer Science (Special), 116-125, (2021).
• [40] Sünnetci, K.M., Alkan, A., “Senkron modülasyon tekniklerine uygulanabilen KNN ve Karar Ağaçları tabanlı SPPM demodülatörler”, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, (2021).