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Facial expression recognition is an important technology in realizing emotional communication between humans and machines [1][2][3]. We address the following issues related to the recognition of emotions from facial expressions. First, facial expression patterns are considered as physical parameters. Expressions convey personality, and as physical parameters, facial expression patterns vary among individuals. Hence, the classification of facial expressions is fundamentally a problem with an unknown number of categories. Accordingly, the extraction of person-specific facial expression categories using a common person-independent technique is an important issue. Second, emotions are considered as psychological parameters. The facial expression pattern of an individual is unique, but as a psychological parameter, emotion is person-independent and universal. Moreover, the degree of a recognized emotion changes according to the degree of physical change in a facial expression pattern. Therefore, it is important to match the amount of physical change in a person-specific facial expression pattern with the corresponding amount of mental change in order to estimate the degree of emotion. Previously, we proposed a method for generating a personspecific facial expression feature space (facial expression map and emotional map) for facial expression recognition [4][5][6]. The results demonstrated that facial expression maps can assign unique emotional categories to input facial expression images. Further, we showed that emotional maps can quantify degrees of emotion according to the level of change in facial expression patterns. Conversely, units generated on the emotional map were biased towards the training data in the aforementioned facial expression feature space, and an issue remained when performing detailed estimations of emotion. Therefore, this study examines the improvement of the method for generating a facial expression feature space that reduces the bias on feature space and allows for the detailed quantification of emotions. In particular, we focus on the counter propagation network (CPN) [7] mapping space used in the method proposed previously, and investigate the number of dimensions and the size of the CPN mapping space.

Study on the Emotion Quantification Method using the Facial Expression Feature Space