Face mask detection based on MobileNet with transfer learning

Wenjie Fan; Qianhan Gao; Wenqi Li

doi:10.1117/12.2623092

1 December 2021 Face mask detection based on MobileNet with transfer learning

Wenjie Fan, Qianhan Gao, Wenqi Li

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120792H (2021) https://doi.org/10.1117/12.2623092
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

After a few cases of COVID-19 had emerged in Wuhan, China, the disease had rapidly spread all over the world. As the World Health Organization confirmed that COVID-19 is an infective and dangerous respiratory virus, it is important to wear masks to reduce the rate of infections. Thus, a face mask detector is essential to encourage people to wear masks and prevent the virus from spreading. In this paper, we proposed a mask detection model based on transfer learning of MobileNet. This face mask detector can be applied to images, videos, or live video streaming to detect whether the people have worn a mask or not. The model has achieved an accuracy of 99.88% on the training set and 99.64% on the testing set. In addition, we used the K-Fold Cross Validation to evaluate our model, and the average accuracy was 97.4%.

Citation Download Citation

Wenjie Fan, Qianhan Gao, and Wenqi Li "Face mask detection based on MobileNet with transfer learning", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120792H (1 December 2021); https://doi.org/10.1117/12.2623092

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available