An Improved Face Mask-aware Recognition System Based on Deep Learning
9
Issued Date
2022-01-01
Resource Type
ISSN
23673370
eISSN
23673389
Scopus ID
2-s2.0-85135041276
Journal Title
Lecture Notes in Networks and Systems
Volume
462
Start Page
15
End Page
29
Rights Holder(s)
SCOPUS
Bibliographic Citation
Lecture Notes in Networks and Systems Vol.462 (2022) , 15-29
Suggested Citation
Lin C.Y., Rojanasarit A., Thaipisutikul T., Lung C.W., Akhyar F. An Improved Face Mask-aware Recognition System Based on Deep Learning. Lecture Notes in Networks and Systems Vol.462 (2022) , 15-29. 29. doi:10.1007/978-981-19-2211-4_2 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84384
Title
An Improved Face Mask-aware Recognition System Based on Deep Learning
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
Face mask detection and recognition have been incorporated into many applications in daily life, especially during the current COVID-19 pandemic. To mitigate the spread of coronavirus, wearing face masks has become commonplace. However, traditional face detection and recognition systems utilize main facial features such as the mouth, nose, and eyes to determine a person’s identity. Masks make facial detection and recognition tasks more challenging since certain parts of the face are concealed. Yet, how to improve the performance of existing systems with a face mask overlaid on the original face input images remains an open area of inquiry. In this study, we propose an improved face mask-aware recognition system named ‘MAR’ based on deep learning, which can tackle challenges in face mask detection and recognition. MAR consists of five main modules to handle various kinds of input images. We re-train the CenterNet model with our augmented face mask inputs to perform face mask detection and propose four variations on face mask recognition models based on the pre-trained ArcFace to handle facial recognition. Finally, we demonstrate the effectiveness of our proposed models on the VGGFACE2 dataset and achieve a high accuracy score on both detection and recognition tasks.