关键词: 面部识别, 标签分布, 子空间学习, 描述度

Abstract: At present, most existing facial expression recognition methods assume that each facial expression in the training set corresponds to a single emotion, which is typically cast as a classification problem. However, in actual situations, a face expression rarely expresses pure emotion, but often a mixture of different emotions. Therefore, samples of similar expression have a correlation at the emotional level, and this correlation also often leads to ambiguity in the sample??s expression labels. Namely, each emotion sample is associated with a latent label distribution. For this reason, this paper proposes a totally data-driven label distribution learning approach to adaptively learn the latent label distributions. Without any pre-hypothesized label distribution, the relationship between each expression and its multiple emotions can be obtained. This method can get the specific description of each emotion contained in the expression and the mapping of the expression image to the emotion distribution. Experimental results show that the proposed method has high accuracy in facial expression recognition and can effectively deal with the facial emotion analysis problem.

Key words: facial expression recognition, label distribution, subspace learning, description degree