A 3d Model-Based Approach For Fitting Masks To Faces In The Wild
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Je Hyeong | - |
dc.contributor.author | Kim, Hanjo | - |
dc.contributor.author | Kim, Minsoo | - |
dc.contributor.author | Nam, Gi Pyo | - |
dc.contributor.author | Cho, Junghyun | - |
dc.contributor.author | Ko, Hyeong-Seok | - |
dc.contributor.author | Kim, Ig-Jae | - |
dc.date.accessioned | 2023-09-26T09:50:26Z | - |
dc.date.available | 2023-09-26T09:50:26Z | - |
dc.date.created | 2023-07-21 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/191303 | - |
dc.description.abstract | Face recognition now requires a large number of labelled masked face images in the era of this unprecedented COVID19 pandemic. Unfortunately, the rapid spread of the virus has left us little time to prepare for such dataset in the wild. To circumvent this issue, we present a 3D model-based approach called WearMask3D for augmenting face images of various poses to the masked face counterparts. Our method proceeds by first fitting a 3D morphable model on the input image, second overlaying the mask surface onto the face model and warping the respective mask texture, and last projecting the 3D mask back to 2D. The mask texture is adapted based on the brightness and resolution of the input image. By working in 3D, our method can produce more natural masked faces of diverse poses from a single mask texture. To compare precisely between different augmentation approaches, we have constructed a dataset comprising masked and unmasked faces with labels called MFW-mini. Experimental results demonstrate WearMask3D 1 produces more realistic masked faces, and utilizing these images for training leads to state-of-the-art recognition accuracy for masked faces. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | A 3d Model-Based Approach For Fitting Masks To Faces In The Wild | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Je Hyeong | - |
dc.identifier.bibliographicCitation | IEEE International Conference on Image Processing, pp.235 - 239 | - |
dc.relation.isPartOf | IEEE International Conference on Image Processing | - |
dc.citation.title | IEEE International Conference on Image Processing | - |
dc.citation.startPage | 235 | - |
dc.citation.endPage | 239 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceeding | - |
dc.description.journalClass | 3 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | other | - |
dc.identifier.url | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9506069 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.