Emotion Distribution Recognition from Facial Expressions

Most existing facial expression recognition methods assume the availability of a single emotion for each expression in the training set. However, in practical applications, an expression rarely expresses pure emotion, but often a mixture of different emotions. To address this problem, this paper deals with a more common case where multiple emotions are associated to each expression. The key idea is to learn the specific description degrees of all basic emotions for each expression and the mapping from the expression images to the emotion distributions by the proposed emotion distribution learning (EDL) method.The databases used in the experiments are the s-JAFFE database and the s-BU\_3DFE database as they are the databases with explicit scores for each emotion on each expression image. Experimental results show that EDL can effectively deal with the emotion distribution recognition problem and perform remarkably better than the state-of-the-art multi-label learning methods.