Abstract
This paper presents a multi-attribute sparse coding approach for facial expression recognition by regarding Action-Units (AUs) as attributes. AUs describe the movements of individual facial muscles, which are detected from corresponding attribute masks in this work. They can not only be used to de scribe group property which enforces basis selection from groups with the same AUs as best as possible, but also penalize the selection of atoms with the AU distance far away from the target instance. The group constraint and the AU similarity constraint are incorporated into the formulation of l1-minimization to determine the optimal sparse representation for facial expression. Finally, we demonstrate the proposed algorithm through experiments on two facial expression datasets to show the effectiveness and robustness of the proposed method.