A multi-label classification approach for Facial Expression Recognition

Facial Expression Recognition (FER) techniques have already been adopted in numerous multimedia systems. Plenty of previous research assumes that each facial picture should be linked to only one of the predefined affective labels. Nevertheless, in practical applications, few of the expressions are exactly one of the predefined affective states. Therefore, to depict the facial expressions more accurately, this paper proposes a multi-label classification approach for FER and each facial expression would be labeled with one or multiple affective states. Meanwhile, by modeling the relationship between labels via Group Lasso regularization term, a maximum margin multi-label classifier is presented and the convex optimization formulation guarantees a global optimal solution. To evaluate the performance of our classifier, the JAFFE dataset is extended into a multi-label facial expression dataset by setting threshold to its continuous labels marked in the original dataset and the labeling results have shown that multiple labels can output a far more accurate description of facial expression. At the same time, the classification results have verified the superior performance of our algorithm.