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Spontaneous and Non-Spontaneous 3D Facial Expression Recognition Using a Statistical Model with Global and Local Constraints

In this paper, we propose a novel method for 3D facial expression recognition based on a statistical shape model with global and local constraints. We show that the combination of the global shape of the face, along with local shape index-based information can be used to recognize a range of expressions. These expressions include happiness, sadness, surprise, embarrassment, fear, nervousness, anger, disgust, and pain. We give insights into which features are important for facial expression recognition through statistical analysis. We also show that our proposed method outperforms the current state-of-the-art methods on spontaneous and non-spontaneous facial data.