Paper Info
Title
Local Relationship Learning With Person-Specific Shape Regularization For Facial Action Unit Detection
Abstract
Encoding individual facial expressions via action units (AUs) coded by the Facial Action Coding System (FACS) has been found to be an effective approach in resolving the ambiguity issue among different expressions. While a number of methods have been proposedfor AU detection, robust AU detection in the wild remains a challenging problem because of the diverse baseline AU intensities across individual subjects, and the weakness of appearance signal ofAUs. To resolve these issues, in this work, we propose a novel AU detection method by utilizing local information and the relationship of individual local face regions. Through such a local relationship learning, we expect to utilize rich local information to improve the AU detection robustness against the potential perceptual inconsistency of individual local regions. In addition, considering the diversity in the baseline AU intensities of individual subjects, we further regularize local relationship learning via person-specific face shape information, i.e., reducing the influence of person-specific shape information, and obtaining more AU discriminative features. The proposed approach outperforms the state-of-the-art methods on two widely used AU detection datasets in the public domain (BP4D and DISFA).
Year
DOI
Venue
2019
10.1109/CVPR.2019.01219
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
ISSN
Computer vision,Pattern recognition,Computer science,Regularization (mathematics),Artificial intelligence
Conference
1063-6919
Citations 
PageRank 
References 
3
0.37
0
Authors
5
Name
Order
Citations
PageRank
Xuesong Niu1123.54
Hu Han275240.02
Songfan Yang334317.48
Yan Huang422627.65
Shiguang Shan56322283.75