Multi-label learning with prior knowledge for facial expression analysis
Facial expression is one of the most expressive ways to display human emotions. Facial expression analysis (FEA) has been broadly studied in the past decades. In our daily life, few of the facial expressions are exactly one of the predefined affective states but are blends of several basic expressions. Even though the concept of blended emotions has been proposed years ago, most researchers did not deal with FEA as a multiple outputs problem yet. In this paper, multi-label learning algorithm for FEA is proposed to solve this problem. Firstly, to depict facial expressions more effectively, we model FEA as a multi-label problem, which depicts all facial expressions with multiple continuous values and labels of predefined affective states. Secondly, in order to model FEA jointly with multiple outputs, multi-label Group Lasso regularized maximum margin classifier (GLMM) and Group Lasso regularized regression (GLR) algorithms are proposed which can analyze all facial expressions at one time instead of modeling as a binary learning problem. Thirdly, to improve the effectiveness of our proposed model used in video sequences, GLR is further extended to be a Total Variation and Group Lasso based regression model (GLTV) which adds a prior term (Total Variation term) in the original model. JAFFE dataset and the extended Cohn Kanade (CK+) dataset have been used to verify the superior performance of our approaches with common used criterions in multi-label classification and regression realms.
facial expression analysis group lasso total variation multi-label classification multi-label regression