In this paper, we present a common framework for realtime action unit detection and emotion recognition that we have developed for the emotion recognition and action unit detection sub-challenges of the FG 2011 Facial Expression Recognition and Analysis Challenge. For these tasks we employed a local appearance-based face representation approach using discrete cosine transform, which has been shown to be very effective and robust for face recognition. Using these features, we trained multiple one-versus-all support vector machine classifiers corresponding to the individual classes of the specific task. With this framework we achieve 24.2% and 7.6% absolute improvement over the overall baseline results on the emotion recognition and action unit detection sub-challenge, respectively.
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