Face recognition under mask-wearing based on residual inception networks
2
Issued Date
2022-01-01
Resource Type
eISSN
22108327
Scopus ID
2-s2.0-85128457287
Journal Title
Applied Computing and Informatics
Rights Holder(s)
SCOPUS
Bibliographic Citation
Applied Computing and Informatics (2022)
Suggested Citation
Moungsouy W., Tawanbunjerd T., Liamsomboon N., Kusakunniran W. Face recognition under mask-wearing based on residual inception networks. Applied Computing and Informatics (2022). doi:10.1108/ACI-09-2021-0256 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84398
Title
Face recognition under mask-wearing based on residual inception networks
Author's Affiliation
Other Contributor(s)
Abstract
Purpose: This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask. Design/methodology/approach: The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing. Findings: The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks. Originality/value: The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.