Development of a face mask detection pipeline for mask-wearing monitoring in the era of the COVID-19 pandemic: A modular approach
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85136179439
Journal Title
2022 19th International Joint Conference on Computer Science and Software Engineering, JCSSE 2022
Rights Holder(s)
SCOPUS
Bibliographic Citation
2022 19th International Joint Conference on Computer Science and Software Engineering, JCSSE 2022 (2022)
Suggested Citation
Sommana B., Watchareeruetai U., Ganguly A., Earp S.W.F., Kitiyakara T., Boonmanunt S., Thammasudjarit R. Development of a face mask detection pipeline for mask-wearing monitoring in the era of the COVID-19 pandemic: A modular approach. 2022 19th International Joint Conference on Computer Science and Software Engineering, JCSSE 2022 (2022). doi:10.1109/JCSSE54890.2022.9836283 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84373
Title
Development of a face mask detection pipeline for mask-wearing monitoring in the era of the COVID-19 pandemic: A modular approach
Author's Affiliation
Other Contributor(s)
Abstract
During the SARS-Cov-2 pandemic, mask-wearing became an effective tool to prevent spreading and contracting the virus. The ability to monitor the mask-wearing rate in the population would be useful for determining public health strategies against the virus. In this paper, we present a two-step face mask detection approach consisting of two separate modules: 1) face detection and alignment and 2) face mask classification. This approach allows us to experiment with different combinations of face detection and face mask classification modules. More specifically, we experimented with PyramidKey and RetinaFace as face detectors while maintaining a lightweight backbone for the face mask classification module. Moreover, we also provide a relabeled annotation of the test set of the AIZOO dataset, where we rectified the incorrect labels for some face images. The evaluation results on the AIZOO and Moxa 3K datasets show that the proposed face mask detection pipeline surpassed the state-of-the-art methods. The proposed pipeline also yielded a higher mAP on the relabeled test set of the AIZOO dataset than the original test set. Since we trained the proposed model using in-the-wild face images, we can successfully deploy our model to monitor the mask-wearing rate using public CCTV images.