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2003
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ICA and Gabor representation for facial expression recognition

10 years 8 days ago
ICA and Gabor representation for facial expression recognition
Two hybrid systems for classifying seven categories of human facial expression are proposed. The £rst 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 coef£cients of these combinations are fed up into SVMs instead of an original feature vector comprised of grayscale image pixel values. The classi£cation accuracy of this system is compared against that of baseline techniques that combine ICA with either two-class cosine similarity classi£ers or two-class maximum correlation classi£ers, when we classify facial expressions into these seven classes. We found that, ICA decomposition combined with SVMs outperforms the aforementioned baseline classi£ers. The second system proposed operates in two steps: £rst, a set of Gabor wavelets (GWs) is applied to the original face image database and, second, the new features obtained are...
Ioan Buciu, Constantine Kotropoulos, Ioannis Pitas
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where ICIP
Authors Ioan Buciu, Constantine Kotropoulos, Ioannis Pitas
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