  The aim of this research is development of rule based decision model for
emotion recognition. This research also proposes using the rules for augmenting
inter-corporal recognition accuracy in multimodal systems that use supervised
learning techniques. The classifiers for such learning based recognition
systems are susceptible to over fitting and only perform well on intra-corporal
data. To overcome the limitation this research proposes using rule based model
as an additional modality. The rules were developed using raw feature data from
visual channel, based on human annotator agreement and existing studies that
have attributed movement and postures to emotions. The outcome of the rule
evaluations was combined during the decision phase of emotion recognition
system. The results indicate rule based emotion recognition augment recognition
accuracy of learning based systems and also provide better recognition rate
across inter corpus emotion test data.
