An improved boundary-aware face alignment using stacked dense U-Nets

Facial landmark localization is still a challenge task in the unconstrained environment with influences of significant variation conditions such as facial pose, shape, expression, illumination, and occlusions. In this work, we present an improved boundary-aware face alignment method by using stacked...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced robotic systems 2020-07, Vol.17 (4)
Main Authors: Ye, Jianghao, Cui, Ying, Pan, Xiang, Zheng, Herong, Guo, Dongyan, Ren, Yanan
Format: Article
Language:English
Subjects:
Alignment
Feature extraction
Landmarks
Message passing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Facial landmark localization is still a challenge task in the unconstrained environment with influences of significant variation conditions such as facial pose, shape, expression, illumination, and occlusions. In this work, we present an improved boundary-aware face alignment method by using stacked dense U-Nets. The proposed method consists of two stages: a boundary heatmap estimation stage to learn the facial boundary lines and a facial landmark localization stage to predict the final face alignment result. With the constraint of boundary lines, facial landmarks are unified as a whole facial shape. Hence, the unseen landmarks in a shape with occlusions can be better estimated by message passing with other landmarks. By introducing the stacked dense U-Nets for feature extraction, the capacity of the model is improved. Experiments and comparisons on public datasets show that the proposed method obtains better performance than the baselines, especially for facial images with large pose variation, shape variation, and occlusions.
ISSN:1729-8806
1729-8814
DOI:10.1177/1729881420940900