  Designing a lightweight and robust portrait segmentation algorithm is an
important task for a wide range of face applications. However, the problem has
been considered as a subset of the object segmentation problem. bviously,
portrait segmentation has its unique requirements. First, because the portrait
segmentation is performed in the middle of a whole process of many realworld
applications, it requires extremely lightweight models. Second, there has not
been any public datasets in this domain that contain a sufficient number of
images with unbiased statistics. To solve the problems, we introduce a new
extremely lightweight portrait segmentation model consisting of a two-branched
architecture based on the concentrated-comprehensive convolutions block. Our
method reduces the number of parameters from 2.1M to 37.7K (around 98.2%
reduction), while maintaining the accuracy within a 1% margin from the
state-of-the-art portrait segmentation method. In our qualitative and
quantitative analysis on the EG1800 dataset, we show that our method
outperforms various existing lightweight segmentation models. Second, we
propose a simple method to create additional portrait segmentation data which
can improve accuracy on the EG1800 dataset. Also, we analyze the bias in public
datasets by additionally annotating race, gender, and age on our own. The
augmented dataset, the additional annotations and code are available in
https://github.com/HYOJINPARK/ExtPortraitSeg .
