Real-Time Detection of Face Mask Using Convolutional Neural Network

Real-Time Detection of Face Mask Using Convolutional Neural Network

mask detection; CNN; face detection; face recognition; pollution

avoid exposure to air pollution, and protect the face from the adverse effects of sunlight. However, many people are still
ignorant about the importance of wearing masks for health. This study aims to detect whether or not to use masks in real-time
by proposing a deep learning model to reduce illness and death caused by air pollution. The convolutional Neural Network
(CNN) method was used in this research to detect facial recognition using a mask and not using a mask. The public dataset
used in this research consists of 1300 images with 650 data using masks and 650 data without masks. The results of this study
show that the proposed CNN method works well in detecting masked and non-masked faces in real time. The proposed method
obtains an accuracy value of 97.5% at epoch 50. Previous research on mask detection using the Eigenface method yielded an
accuracy of 88.89%, and another study using the Viola-Jones method yielded an accuracy of 95.5%. It can be concluded that
this research can increase the accuracy value of previous studies. So, this research is feasible to be applied to the detection of
mask use in real time.

Imam Husni Al Amin, Deva Ega Marinda, Edy Winarno,Dewi Handayani U.N, Veronica Lusiana

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

June 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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