iBugMask: Face Parsing in the Wild (ImaVis 2021)

iBugMask is an in-the-wild face parsing dataset that contains 1,000 challenging face images and manually annotated labels for 11 semantic classes: background, facial skin, left/right brow, left/right eye, nose, upper/lower lip, inner mouth, and hair. The images are curated from challenging in-the-wild face alignment datasets, including 300W and Menpo. Compared with the existing face parsing datasets, iBugMask contains in-the-wild scenarios such as “party” and “conference”, which include more challenging appearance variations or multiple faces. There is a larger number of profile faces. More expressions other than ”neutral” and ”smile” are also included (e.g. ”surprise” and ”scream”). The dataset can be downloaded here.

Some examples



    title={RoI Tanh-polar Transformer Network for Face Parsing in the Wild},
    author={Yiming Lin and Jie Shen and Yujiang Wang and Maja Pantic},