TY - GEN
T1 - Twitter Face Image Mining for Recognition of Different Face Mask Types
AU - Arroyo-Rojas, Ulises
AU - Jimenez-Martinez, Miguel
AU - Benitez-Garcia, Gibran
AU - Olivares-Mercado, Jesus
AU - Takahashi, Hiroki
N1 - Publisher Copyright:
© 2022 The authors and IOS Press. All rights reserved.
PY - 2022/9/14
Y1 - 2022/9/14
N2 - In the current pandemic of coronavirus disease (COVID-19), an effective way to prevent the transmission and infection of the virus is the proper use of face masks. However, the different types of masks provide different degrees of protection. For instance, valved masks protect the user but do not help to stop the transmission. Hence, the automatic recognition of face mask types may benefit applications that control access to facilities where a certain facepiece is required. In this paper, we propose a Twitter mining framework to gather a large-scale dataset of masked faces suitable to train deep learning-based models for face mask recognition. We employ a keyword-based selection where non-face images are discarded by an efficient face detector (Retinaface). Finally, we train a state-of-the-art CNN architecture (ConvNeXt) for recognizing the wearing mask. We also present a brief analysis of more than two million image-based tweets acquired over two years since the beginning of the pandemic. The code of the proposed framework and a preliminary dataset of more than 10K faces (manually annotated into unmasked, surgical, cloth, respirators, and valved masks) are available on github.com/GibranBenitez/FaceMask Twitter.
AB - In the current pandemic of coronavirus disease (COVID-19), an effective way to prevent the transmission and infection of the virus is the proper use of face masks. However, the different types of masks provide different degrees of protection. For instance, valved masks protect the user but do not help to stop the transmission. Hence, the automatic recognition of face mask types may benefit applications that control access to facilities where a certain facepiece is required. In this paper, we propose a Twitter mining framework to gather a large-scale dataset of masked faces suitable to train deep learning-based models for face mask recognition. We employ a keyword-based selection where non-face images are discarded by an efficient face detector (Retinaface). Finally, we train a state-of-the-art CNN architecture (ConvNeXt) for recognizing the wearing mask. We also present a brief analysis of more than two million image-based tweets acquired over two years since the beginning of the pandemic. The code of the proposed framework and a preliminary dataset of more than 10K faces (manually annotated into unmasked, surgical, cloth, respirators, and valved masks) are available on github.com/GibranBenitez/FaceMask Twitter.
KW - ConvNeXt
KW - Face image mining
KW - Face mask recognition
KW - Retinaface
KW - Twitter image mining
UR - http://www.scopus.com/inward/record.url?scp=85139801624&partnerID=8YFLogxK
U2 - 10.3233/FAIA220260
DO - 10.3233/FAIA220260
M3 - Contribución a la conferencia
AN - SCOPUS:85139801624
T3 - Frontiers in Artificial Intelligence and Applications
SP - 298
EP - 309
BT - New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
A2 - Fujita, Hamido
A2 - Watanobe, Yutaka
A2 - Azumi, Takuya
PB - IOS Press BV
T2 - 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
Y2 - 20 September 2022 through 22 September 2022
ER -