Generation of facial expression map based on topological characteristics of face images

This paper proposes a generation method of a Facial Expression Map (FEMap) using a Self-Organizing Maps (SOM) of unsupervised learning and Counter Propagation Networks (CPN) of supervised learning. First, the topological change of a face pattern in the expressional process of facial expression is learned hierarchically using the SOM of a narrow mapping space. The number of subject-specific facial expression categories and the representative images of each category are generated. Next, these images are learned using the CPN of a large mapping space. A category map that expresses the topological characteristics of facial expression is generated. Finally, psychological significance based on a neutral expression and six basic emotions is assigned to each category. This paper defines this category map as an FEMap. Experimental results for six subjects show that the proposed method can generate a subject-specific FEMap based on the topological characteristics of facial expression appearing on face images.