Bilinear Kernel Reduced Rank Regression for Facial Expression Synthesis
Abstract
In the last few years, Facial Expression Synthesis (FES) has been a flourishing area of research driven by applications in character animation, computer games, and human computer interaction. This paper proposes a photorealistic FES method based on Bilinear Kernel Reduced Rank Regression (BKRRR). BKRRR learns a high-dimensional mapping between the appearance of a neutral face and a variety of expressions (e.g. smile, surprise, squint). There are two main contributions in this paper: (1) Propose BKRRR for FES. Several algorithms for learning the parameters of BKRRR are evaluated. (2) Propose a new method to preserve subtle person-specific facial characteristics (e.g. wrinkles, pimples). Experimental results on the CMUMulti-PIE database and pictures taken with a regular camera show the effectiveness of our approach.
BibTeX
@conference{Huang-2010-120928,author = {D. Huang and F. De la Torre},
title = {Bilinear Kernel Reduced Rank Regression for Facial Expression Synthesis},
booktitle = {Proceedings of (ECCV) European Conference on Computer Vision},
year = {2010},
month = {September},
pages = {364 - 377},
}