A semantic facial expression intensity descriptor based on information granules.

This paper investigates a granular description for facial expression intensity in which characterization of motion-based expression features is presented by a collection of information granules. First, the motion-based features are extracted based on the facial landmarks to encode the expression intensity. Secondly, the semantic concepts are then generated by involving various mechanisms of fuzzy clustering based on the motion-based features. The resulting clusters can be viewed as numeric prototypes of the descriptors. The information granules are formed around the prototypes according to the principle of justifiable granularity, which is the fundamental idea of Granular Computing. The proposed granular descriptor for expression intensity is tested on the BU-3DFE database, and the experimental results illustrate that the proposed descriptors based on information granules not only can characterize the semantics of facial changes caused by expressions, but also can facilitate the semantic estimation of facial expression intensity.