TY - GEN
T1 - TFM a Dataset for Detection and Recognition of Masked Faces in the Wild
AU - Benitez-Garcia, Gibran
AU - Takahashi, Hiroki
AU - Jimenez-Martinez, Miguel
AU - Olivares-Mercado, Jesus
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/12/13
Y1 - 2022/12/13
N2 - Droplet transmission is one of the leading causes of the spread of respiratory infections, such as coronavirus disease (COVID-19). The proper use of face masks is an effective way to prevent the transmission of such diseases. Nonetheless, different types of masks provide various degrees of protection. Hence, automatic recognition of face mask types may benefit the control access to facilities where a specific protection degree is required. In the last two years, several deep learning models have been proposed for face mask detection and properly wearing mask recognition. However, the current publicly available datasets do not consider the different mask types and occasionally lack real-world elements needed to train robust models. In this paper, we introduce a new dataset named TFM with sufficient size and variety to train and evaluate deep learning models for face mask detection and recognition. This dataset contains more than 135,000 annotated faces from about 100,000 photographs taken in the wild. We consider four mask types (cloth, respirators, surgical and valved) as well as unmasked faces, of which up to six can appear in a single image. The photographs were mined from Twitter within two years since the beginning of the COVID-19 pandemic. Thus, they include diverse scenes with real-world variations in background and illumination. With our dataset, the performance of four state-of-the-art object detection models is evaluated. The experimental results show that YOLOv5 can achieve about 90% of mAP@0.5, demonstrating that the TFM dataset can be used to train robust models and may help the community step forward in detecting and recognizing masked faces in the wild. Our dataset and pre-trained models used in the evaluation will be available upon the publication of this paper.
AB - Droplet transmission is one of the leading causes of the spread of respiratory infections, such as coronavirus disease (COVID-19). The proper use of face masks is an effective way to prevent the transmission of such diseases. Nonetheless, different types of masks provide various degrees of protection. Hence, automatic recognition of face mask types may benefit the control access to facilities where a specific protection degree is required. In the last two years, several deep learning models have been proposed for face mask detection and properly wearing mask recognition. However, the current publicly available datasets do not consider the different mask types and occasionally lack real-world elements needed to train robust models. In this paper, we introduce a new dataset named TFM with sufficient size and variety to train and evaluate deep learning models for face mask detection and recognition. This dataset contains more than 135,000 annotated faces from about 100,000 photographs taken in the wild. We consider four mask types (cloth, respirators, surgical and valved) as well as unmasked faces, of which up to six can appear in a single image. The photographs were mined from Twitter within two years since the beginning of the COVID-19 pandemic. Thus, they include diverse scenes with real-world variations in background and illumination. With our dataset, the performance of four state-of-the-art object detection models is evaluated. The experimental results show that YOLOv5 can achieve about 90% of mAP@0.5, demonstrating that the TFM dataset can be used to train robust models and may help the community step forward in detecting and recognizing masked faces in the wild. Our dataset and pre-trained models used in the evaluation will be available upon the publication of this paper.
KW - Twitter image mining
KW - datasets
KW - face mask detection
KW - face mask recognition
UR - http://www.scopus.com/inward/record.url?scp=85145776378&partnerID=8YFLogxK
U2 - 10.1145/3551626.3564957
DO - 10.1145/3551626.3564957
M3 - Contribución a la conferencia
AN - SCOPUS:85145776378
T3 - Proceedings of the 4th ACM International Conference on Multimedia in Asia, MMAsia 2022
BT - Proceedings of the 4th ACM International Conference on Multimedia in Asia, MMAsia 2022
PB - Association for Computing Machinery, Inc
T2 - 4th ACM International Conference on Multimedia in Asia, MMAsia 2022
Y2 - 13 December 2022 through 16 December 2022
ER -