An adaptive group lasso based multi-label regression approach for facial expression analysis

In the realm of facial expression analysis, numerous attempts have been made to link each facial picture to one affective category. Nevertheless, in our daily life, few of the facial expressions are exactly one of the predefined affective states. Therefore, to analyze the facial expressions more effectively, this paper proposes an Adaptive Group Lasso based Multilabel Regression approach, which depicts each facial expression with multiple continuous values of predefined affective states. Adaptive Group Lasso is adopted to depict the relationship between different labels which different facial expressions share some same affective facial areas (patches). Moreover, to solve the multi-label regression problem, a convex optimization formulation is presented, which would guarantee a global optimal solution. The experiment results based on JAFFE dataset have verified the superior performance of our approach.