Paper Info
Title
Facial Expression-Aware Face Frontalization
Abstract
Face frontalization is a rising technique for view-invariant face analysis. It enables a non-frontal facial image to recover its general facial appearances to frontal view. A few pioneering works have been proposed very recently. However, face frontalization with detailed facial expression recovering is still very challenging due to the non-linear relationships between head-pose and expression variations. In this paper, we propose a novel facial expression-aware face frontalization method aiming at reconstructing the frontal view while maintaining vivid appearances with regards to facial expressions. First of all, we design multiple face shape models as the reference templates in order to fit in with various shape of facial expressions. Each template describes a set of typical facial actions referred to Facial Action Coding System (FACS). Then a template matching strategy is applied by measuring a weighted Chi Square error such that the input image can be matched with the most approximate template. Finally, Robust Statistical face Frontalization (RSF) method is employed for the task of frontal view recovery. This method is validated on a spontaneous facial expression database and the experimental results show that the proposed method outperforms the state-ofthe- art methods.
Year
DOI
Venue
2016
10.1007/978-3-319-54187-7_25
COMPUTER VISION - ACCV 2016, PT III
Field
DocType
Volume
Template matching,Computer vision,Facial Action Coding System,Pattern recognition,Computer science,Facial expression,Artificial intelligence,Template,Face analysis
Conference
10113
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Yiming Wang1112.17
Hui Yu212821.50
Junyu Dong339377.68
Brett Stevens4426.10
Honghai Liu51974178.69