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We propose a novel approach to the detection and classification of human facial expressions using a morphable 3D model. We acquire the various expressions of an individual using a face scanner that produces textured 3D meshes using stereoscopic reconstruction. A morphable expression model (MEM), that incorporates emotion-dependent face variations in terms of morphing parameters, is then computed by establishing correspondence among the emotive faces. These morphing parameters are used for emotion recognition and classification. We demonstrate that the different facial expressions correspond to distinct clusters in the expression space.