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

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, based upon the user preferences and the generalizations formed from prior input is introduced.

Abstract

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.