Case-based reasoning for user-profiled recognition of emotions from face images

To allow for rich and sometimes subtle shadings of emotion that humans recognize in a facial expression, user-profiled recognition of emotions from images of faces is needed. In this work, we introduce a case-based reasoning system capable of classifying facial expressions (given in terms of facial muscle actions) into the emotion categories learned from the user. The utilized case base is a dynamic, incrementally self-organizing-event-content-addressable memory that allows fact retrieval and evaluation of encountered events, based upon the user preferences and the generalizations formed from prior input. Two versions of a prototype system are presented: one aims at recognition of six "universal" emotions and the other aims at recognition of affective states learned from the user. Validation studies suggest that in 100% and in 97% of the test cases, respectively, interpretations produced by the system are consistent with those of the two users who trained the two versions of the prototype system.