Toward High Quality Facial Representation Learning
Yue Wang Jinlong Peng Jiangning Zhang Ran Yi Liang Liu Yabiao Wang Chengjie Wang
Face analysis tasks have a wide range of applications, but the universal
facial representation has only been explored in a few works. In this paper, we
explore high-performance pre-training methods to boost the face analysis tasks
such as face alignment and face parsing. We propose a self-supervised
pre-training framework, called \textbf{\it Mask Contrastive Face (MCF)}, with
mask image modeling and a contrastive strategy specially adjusted for face
domain tasks. To improve the facial representation quality, we use feature map
of a pre-trained visual backbone as a supervision item and use a partially
pre-trained decoder for mask image modeling. To handle the face identity during
the pre-training stage, we further use random masks to build contrastive
learning pairs. We conduct the pre-training on the LAION-FACE-cropped dataset,
a variants of LAION-FACE 20M, which contains more than 20 million face images
from Internet websites. For efficiency pre-training, we explore our framework
pre-training performance on a small part of LAION-FACE-cropped and verify the
superiority with different pre-training settings. Our model pre-trained with
the full pre-training dataset outperforms the state-of-the-art methods on
multiple downstream tasks. Our model achieves 0.932 NME$_{diag}$ for AFLW-19
face alignment and 93.96 F1 score for LaPa face parsing. Code is available at
https://github.com/nomewang/MCF.