Co-Clustering to Reveal Salient Facial Features for Expression Recognition

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Facial expressions are a strong visual intimation of gestural behaviors. The intelligent ability to learn these non-verbal cues of the humans is the key characteristic to develop efficient human computer interaction systems. Extracting an effective representation from facial expression images is a crucial step that impacts the recognition accuracy. In this paper, we propose a novel feature selection strategy using singular value decomposition (SVD) based co-clustering to search for the most salient regions in terms of facial features that possess a high discriminating ability among all expressions. To the best of our knowledge, this is the first known attempt to explicitly perform co-clustering in the facial expression recognition domain. In our method, Gabor filters are used to extract local features from an image and then discriminant features are selected based on the class membership in co-clusters. Experiments demonstrate that co-clustering localizes the salient regions of the face image. Not only does the procedure reduce the dimensionality but also improves the recognition accuracy. Experiments on CK plus, JAFFE and MMI databases validate the existence and effectiveness of these learned facial features.