TY - GEN
T1 - An Improved Face Mask-aware Recognition System Based on Deep Learning
AU - Lin, Chih Yang
AU - Rojanasarit, Amornthep
AU - Thaipisutikul, Tipajin
AU - Lung, Chi Wen
AU - Akhyar, Fityanul
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Deep learning
KW - Face mask detection
KW - Face mask recognition
UR - https://www.scopus.com/pages/publications/85135041276
U2 - 10.1007/978-981-19-2211-4_2
DO - 10.1007/978-981-19-2211-4_2
M3 - Conference contribution
AN - SCOPUS:85135041276
SN - 9789811922107
T3 - Lecture Notes in Networks and Systems
SP - 15
EP - 29
BT - Data Science and Security - Proceedings of IDSCS 2022
A2 - Shukla, Samiksha
A2 - Gao, Xiao-Zhi
A2 - Kureethara, Joseph Varghese
A2 - Mishra, Durgesh
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Data Science, Computation, and Security, IDSCS 2022
Y2 - 11 February 2022 through 12 February 2022
ER -