Human emotion recognition using a deformable 3D facial expression model

Automatic emotion recognition from facial expression is one of the most intensively researched topics in affective computing and human-computer interaction. However, due to the lack of 3D feature and dynamic analysis the functional aspect of affective computing is insufficient for natural interaction. This paper presents an automatic emotion recognition approach from video sequences based on a fiducial point controlled 3D facial model. As a physics-based transformation, elastic body spline technology is applied on a facial mesh to generate a smooth warp that reflects the control point corresponding to the displacement of fiducial points. It also extracts the deformation feature from the realistic emotional expressions. Discriminative Isomap based classification is used to embed the deformation feature into a low dimensional manifold that spans in an expression space with one neutral and six emotion class centers. The final decision is made by computing the Nearest Class Center of the feature space.