Je Hyeong Hong Hanjo Kim MINSOO KIM Gi Pyo Nam Junghyun Cho Hyeong-Seok Kim Kim, Ig-Jae 2024-01-12T03:45:05Z 2024-01-12T03:45:05Z 2021-12-14 2021-09 1522-4880 https://pubs.kist.re.kr/handle/201004/77351 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 WearMask3D1produces more realistic masked faces, and utilizing these images for training leads to state-of-the art recognition accuracy for masked faces. English IEEE A 3d Model-Based Approach For Fitting Masks To Faces In The Wild Conference 10.1109/ICIP42928.2021.9506069 1 IEEE International Conference on Image Processing (ICIP), pp.235 - 239 IEEE International Conference on Image Processing (ICIP) 235 239 US Vitrual Conference 2021-09-19 2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) 000819455100048 2-s2.0-85125565756