TY - GEN
T1 - Eyeglass Frame Segmentation for Face Image Processing
AU - Miura, Kanta
AU - Miyamoto, Takamichi
AU - Sakurai, Kazuyuki
AU - Ito, Koichi
AU - Aoki, Takafumi
N1 - Publisher Copyright:
© 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).
PY - 2022
Y1 - 2022
N2 - Many people commonly wear eyeglasses on their face, masking the area around the eyes. The lens part of the eyeglasses can often be visible through the back area, while the frame part of the eyeglasses completely hides the back area, resulting in degrading the performance of face image processing. By taking the eyeglass frame into account in face image processing, we can not only improve the accuracy of recognition and analysis, but also apply it to automatic quality assessment in standardized photos such as passport photos. In this paper, we propose an eyeglass frame segmentation method using the combination of U-Net and PSPNet. We also propose a novel data augmentation method to increase the number of face images with eyeglasses. Through a set of experiments using CelebAMask-HQ, we demonstrate that the proposed method exhibits the efficient performance in the segmentation of eyeglass frames.
AB - Many people commonly wear eyeglasses on their face, masking the area around the eyes. The lens part of the eyeglasses can often be visible through the back area, while the frame part of the eyeglasses completely hides the back area, resulting in degrading the performance of face image processing. By taking the eyeglass frame into account in face image processing, we can not only improve the accuracy of recognition and analysis, but also apply it to automatic quality assessment in standardized photos such as passport photos. In this paper, we propose an eyeglass frame segmentation method using the combination of U-Net and PSPNet. We also propose a novel data augmentation method to increase the number of face images with eyeglasses. Through a set of experiments using CelebAMask-HQ, we demonstrate that the proposed method exhibits the efficient performance in the segmentation of eyeglass frames.
UR - http://www.scopus.com/inward/record.url?scp=85146303623&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146303623&partnerID=8YFLogxK
U2 - 10.23919/APSIPAASC55919.2022.9980279
DO - 10.23919/APSIPAASC55919.2022.9980279
M3 - Conference contribution
AN - SCOPUS:85146303623
T3 - Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
SP - 1572
EP - 1576
BT - Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
Y2 - 7 November 2022 through 10 November 2022
ER -