  Face parsing, which is to assign a semantic label to each pixel in face
images, has recently attracted increasing interest due to its huge application
potentials. Although many face related fields (e.g., face recognition and face
detection) have been well studied for many years, the existing datasets for
face parsing are still severely limited in terms of the scale and quality,
e.g., the widely used Helen dataset only contains 2,330 images. This is mainly
because pixel-level annotation is a high cost and time-consuming work,
especially for the facial parts without clear boundaries. The lack of accurate
annotated datasets becomes a major obstacle in the progress of face parsing
task. It is a feasible way to utilize dense facial landmarks to guide the
parsing annotation. However, annotating dense landmarks on human face
encounters the same issues as the parsing annotation. To overcome the above
problems, in this paper, we develop a high-efficiency framework for face
parsing annotation, which considerably simplifies and speeds up the parsing
annotation by two consecutive modules. Benefit from the proposed framework, we
construct a new Dense Landmark Guided Face Parsing (LaPa) benchmark. It
consists of 22,000 face images with large variations in expression, pose,
occlusion, etc. Each image is provided with accurate annotation of a
11-category pixel-level label map along with coordinates of 106-point
landmarks. To the best of our knowledge, it is currently the largest public
dataset for face parsing. To make full use of our LaPa dataset with abundant
face shape and boundary priors, we propose a simple yet effective
Boundary-Sensitive Parsing Network (BSPNet). Our network is taken as a baseline
model on the proposed LaPa dataset, and meanwhile, it achieves the
state-of-the-art performance on the Helen dataset without resorting to extra
face alignment.
