Multi-view facial expression recognition using local appearance features

In this paper, we present a multi-view facial expression classification system. The system utilizes local features extracted around automatically located facial landmarks using pose-dependent active appearance models. A pose-dependent ensemble of support vector machine classifiers assigns the given sample to one of the six basic expression classes. Extensive experiments have been conducted on the BU-3DFE database, comparing normalized landmark coordinates, discrete cosine transform, local binary patterns, and scale invariant feature transform based features, as well as combinations of shape and appearance features for classification. We evaluate the influence of AAM fitting errors, F-score feature selection, and expression intensity levels on classification accuracy. Features selected from a combination of normalized landmark coordinates and DCT-based features lead to a correct classification rate of 74.1%, outperforming automatic state-of-the-art multi-view expression recognition systems.