Facial Expression Representation and Recognition Using 2DHLDA, Gabor Wavelets, and Ensemble Learning

TL;DR

This paper introduces a robust facial expression recognition method combining 2DHLDA for dimensionality reduction, Gabor wavelets for feature extraction, and ensemble learning for classification, demonstrating superior performance on the Cohn-Kanade database.

Contribution

It presents a novel combination of 2DHLDA, Gabor wavelets, and ensemble learning for improved facial expression recognition, addressing small sample size and stability issues.

Findings

Outperforms existing methods on Cohn-Kanade database

Robust to illumination changes and subtle facial muscle movements

Effectively captures temporal and geometric features

Abstract

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.