Segmentation of Facial Expressions Using Semi-Definite Programming and Generalized Principal Component Analysis
Computer Vision and Pattern Recognition
2009-06-10 v2
Abstract
In this paper, we use semi-definite programming and generalized principal component analysis (GPCA) to distinguish between two or more different facial expressions. In the first step, semi-definite programming is used to reduce the dimension of the image data and "unfold" the manifold which the data points (corresponding to facial expressions) reside on. Next, GPCA is used to fit a series of subspaces to the data points and associate each data point with a subspace. Data points that belong to the same subspace are claimed to belong to the same facial expression category. An example is provided.
Keywords
Cite
@article{arxiv.0906.1763,
title = {Segmentation of Facial Expressions Using Semi-Definite Programming and Generalized Principal Component Analysis},
author = {Behnood Gholami and Allen R. Tannenbaum and Wassim M. Haddad},
journal= {arXiv preprint arXiv:0906.1763},
year = {2009}
}
Comments
Corrected for typos and spacing errors