Portrait segmentation is becoming a hot topic nowadays.
In this paper we propose a novel framework to cope with
the high precision requirements that portrait segmentation
demands on boundary area by deep refinement of the
portrait matting. Our approach introduces three novel
techniques. First, a trimap is proposed by fusing information
coming from two well-known techniques for image
segmentation, i.e., Mask R-CNN and DensePose. Second,
an alpha matting algorithm runs over the previous trimap
generate. From this mate result we generate a couple of
masks, one of them boundary-sensitive kernel, called
boundary and the other one inside-sensitive kernel called
leftover. Third, we refine the portrait by a pre-trained CNNbased
model, followed by a transposed convolution. We
have evaluated our approach on the PFCN dataset as well as
the portrait images collected from COCO dataset.
Experimental results demonstrate the better performance of
our algorithm over previous methods.