Hybrid Model Transfer Learning ResNet50 and Support Vector Machine for Face Mask Detection

Authors

  • Eko Agus Moh. Iqbal Program Specification for Master Study in Computer Science, Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia
  • Ririen Kusumawati Program Specification for Master Study in Computer Science, Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia
  • Irwan Budi Santoso Program Specification for Master Study in Computer Science, Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia

DOI:

https://doi.org/10.25008/ijadis.v4i2.1297

Keywords:

Face Mask, Support Vector Machine, ResNet50, Transfer Learning, HSV, Image processing

Abstract

The Covid-19 virus caused a health crisis in Indonesia. This virus is so deadly that it has caused many fatalities which have caused the whole world including the government to pay major attention to the Covid-19 pandemic. The Indonesian government has issued several policies to prevent the spread of this epidemic, one of which is wearing a mask in public places. One approach that is widely used in the field of computer vision is the Convolutional Neural Network (CNN) transfer learning. In this study, Hybrid Model Transfer Learning ResNet50 and SVM with RGB to HSV preprocessing is presented to detect masks in facial images. This model consists of three process components. The first is preprocessing RGB images to HSV, the second component is for Feature Extraction with ResNet50 and the third is mask classification on face images with Support Vector Machine (SVM). From dataset of 7328 training and testing data were carried out. The first model, without preprocessing the image data with ResNet50, produces an accuracy of 86.52%. The second model, the model with preprocessing converts image data from RGB to HSV with ResNet50 resulting in an accuracy of 99.18%. In the third model, without preprocessing with ResNet50 and SVM which has an accuracy of 90.55%. The fourth model, the model with preprocessing converts image data from RGB to HSV with ResNet50 and SVM resulting in an accuracy of 98.36%.

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Author Biographies

Eko Agus Moh. Iqbal, Program Specification for Master Study in Computer Science, Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia

 

 

 

Irwan Budi Santoso, Program Specification for Master Study in Computer Science, Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia

 

 

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References

P. Boldog, T. Tekeli, Z. Vizi, A. Dénes, F. A. Bartha, and G. Röst, "Risk assessment of novel coronavirus COVID-19 outbreaks outside China," J Clin Med, vol. 9, no. 2, Feb. 2020, doi: 10.3390/jcm9020571. https://doi.org/10.3390/jcm9020571

Nurkholis, "Dampak Pandemi Novel-Corona Virus Disiase (Covid-19) Terhadap Psikologi Dan Pendidikan Serta Kebijakan Pemerintah," Jurnal PGSD, vol. Volume 6 (1) 2020, 2020. https://doi.org/10.32534/jps.v6i1.1035

O. Russakovsky, J. Deng, Z. Huang, A. C. Berg, and L. Fei-Fei, "Detecting avocados to zucchinis: what have we done, and where are we going?," 2015. [Online]. Available: www.image-net.org/challenges/LSVRC/2012/analysis/

https://doi.org/10.1109/ICCV.2013.258

C. Cortes, V. Vapnik, and L. Saitta, "Support-Vector Networks Editor," Kluwer Academic Publishers, 1995. https://doi.org/10.1007/BF00994018

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 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/ICECOCS50124.2020.9314511. https://doi.org/10.1109/ICECOCS50124.2020.9314511

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, vol. 8, no. 7, pp. 1368-1375, Jul. 2020, doi: 10.22214/ijraset.2020.30560.

https://doi.org/10.22214/ijraset.2020.30560

I. B. Venkateswarlu, J. Kakarla, and S. Prakash, "Face mask detection using MobileNet and global pooling block," in 4th IEEE Conference on Information and Communication Technology, CICT 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/CICT51604.2020.9312083. https://doi.org/10.1109/CICT51604.2020.9312083

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 (Lond), vol. 167, Jan. 2021, doi: 10.1016/j.measurement.2020.108288. https://doi.org/10.1016/j.measurement.2020.108288

S. A. Sanjaya and S. A. Rakhmawan, "Face Mask Detection Using MobileNetV2 in the Era of COVID-19 Pandemic," in 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/ICDABI51230.2020.9325631. https://doi.org/10.1109/ICDABI51230.2020.9325631

A. Negi, P. Chauhan, K. Kumar, and R. S. Rajput, "Face Mask Detection Classifier and Model Pruning with Keras-Surgeon," in 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2020 - Proceeding, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/ICRAIE51050.2020.9358337. https://doi.org/10.1109/ICRAIE51050.2020.9358337

K. Suresh, M. B. Palangappa, and S. Bhuvan, "Face Mask Detection by using Optimistic Convolutional Neural Network," in Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, Institute of Electrical and Electronics Engineers Inc., Jan. 2021, pp. 1084-1089. doi: 10.1109/ICICT50816.2021.9358653. https://doi.org/10.1109/ICICT50816.2021.9358653

S. Sethi, M. Kathuria, and T. Kaushik, "Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread," J Biomed Inform, vol. 120, Aug. 2021, doi: 10.1016/j.jbi.2021.103848. https://doi.org/10.1016/j.jbi.2021.103848

G. Moreira, S. A. Magalhães, T. Pinho, F. N. Dos Santos, and M. Cunha, "Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato," Agronomy, vol. 12, no. 2, Feb. 2022, doi: 10.3390/agronomy12020356. https://doi.org/10.3390/agronomy12020356

T. H. Nguyen, T. N. Nguyen, and B. V. Ngo, "A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease," AgriEngineering, vol. 4, no. 4, pp. 871-887, Dec. 2022, doi: 10.3390/agriengineering4040056. https://doi.org/10.3390/agriengineering4040056

Prajna Bhandary, "Mask Classifier," https://github.com/prajnasb/observations, 2020.

Vijay Kumar, "Face Mask Detection," https://www.kaggle.com/datasets/vijaykumar1799/face-mask-detection, 2021.

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Published

2023-09-06

How to Cite

Iqbal, E. A. M., Kusumawati, R., & Santoso, I. B. (2023). Hybrid Model Transfer Learning ResNet50 and Support Vector Machine for Face Mask Detection. International Journal of Advances in Data and Information Systems, 4(2), 125-134. https://doi.org/10.25008/ijadis.v4i2.1297
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