ICA and Gabor representation for facial expression recognition

Two hybrid systems for classifying seven categories of human facial expression are proposed. The first system combines independent component analysis (ICA) and support vector machines (SVMs). The original face image database is decomposed into linear combinations of several basis images, where the corresponding coefficients of these combinations are fed up into SVMs instead of an original feature vector comprised of grayscale image pixel values. The classification accuracy of this system is compared against that of baseline techniques that combine ICA with either two-class cosine similarity classifiers or two-class maximum correlation classifiers, when we classify facial expressions into these seven classes. We found that, ICA decomposition combined with SVMs outperforms the aforementioned baseline classifiers. The second system proposed operates in two steps: first, a set of Gabor wavelets (GWs) is applied to the original face image database and, second, the new features obtained are classified by using either SVMs or cosine similarity classifiers or maximum correlation classifier. The best facial expression recognition rate is achieved when Gabor wavelets are combined with SVMs.