Paper Info
Title
Cascade Support Vector Regression-Based Facial Expression-Aware Face Frontalization
Abstract
The main aim of face frontalization is to synthesize the frontal facial appearances from non-frontal facial images. How to estimate the frontal face-shape is a crucial but very challenging problem in the frontalization task. Most existing methods use a single shape template to fit in with frontal facial appearances, which will result in a loss of expression related information. In this work, we present a novel facial expression-aware face frontalization method which directly learns the pair-wise relations between non-frontal face-shape and its frontal counterpart. The support vector regression is explored to train the pair-wise regression model. Considered the pair-wise relationship is non-linear, an appropriate cascade manner is applied to iteratively adjust and optimize the model. With the estimated frontal shape, facial appearances are synthesized through a texture-fitting process formulated by solving a simple optimization problem. The proposed method has been evaluated on a in-the-wild facial expression database. The experimental results shows an outstanding performance of both visual effects of expression recovery and facial expression recognition.
Year
Venue
Keywords
2017
2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Face frontalization, facial expression-aware, facial expression recognition, support vector regression, facial expression analysis
Field
DocType
ISSN
Computer vision,Facial recognition system,Pattern recognition,Facial expression recognition,Regression analysis,Computer science,Support vector machine,Facial expression,Cascade,Artificial intelligence,Optimization problem
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Yiming Wang1112.17
Hui Yu212821.50
Junyu Dong339377.68
Muwei Jian423530.97
Honghai Liu51974178.69