  In this paper, a novel method for representation and recognition of the
facial expressions in two-dimensional image sequences is presented. We apply a
variation of two-dimensional heteroscedastic linear discriminant analysis
(2DHLDA) algorithm, as an efficient dimensionality reduction technique, to
Gabor representation of the input sequence. 2DHLDA is an extension of the
two-dimensional linear discriminant analysis (2DLDA) approach and it removes
the equal within-class covariance. By applying 2DHLDA in two directions, we
eliminate the correlations between both image columns and image rows. Then, we
perform a one-dimensional LDA on the new features. This combined method can
alleviate the small sample size problem and instability encountered by HLDA.
Also, employing both geometric and appearance features and using an ensemble
learning scheme based on data fusion, we create a classifier which can
efficiently classify the facial expressions. The proposed method is robust to
illumination changes and it can properly represent temporal information as well
as subtle changes in facial muscles. We provide experiments on Cohn-Kanade
database that show the superiority of the proposed method. KEYWORDS:
two-dimensional heteroscedastic linear discriminant analysis (2DHLDA), subspace
learning, facial expression analysis, Gabor wavelets, ensemble learning.
