The fuzzy emotion recognition framework using semantic-linguistic facial features

Emotion recognition through facial expression analysis is an emerging research in Artificial Intelligence which faces many challenges. The problem is the variation of facial expressions that displays human emotions. Humans can subjectively express the same emotions in various ways. To overcome the problem of ambiguity in emotion expression, a fuzzy approach is developed to analyze the facial components in determining the type of emotion. In this study, we proposed a framework for fuzzy emotion recognition as a representation of the psychologist knowledge. Three stages in the fuzzy emotion recognition were facial feature extraction with Active Appearance Model; Semantic-linguistic facial features extraction; fuzzy emotion recognition with Fuzzy Emotion Classification. System performance testing provided the best results on extended Cohn Kanade (CK+) facial expression dataset, with the accuracy of linguistic facial component recognition 0.98, and accuracy of fuzzy emotion recognition 0.90. Testing was also performed on custom-made Indonesian Mixed Emotion Dataset (IMED) which resulted in accuracy of 0.87. The fuzzy emotion recognition has a potential to be applied in various real problems such as virtual counseling, stress detection, lie detection, and e-commerce.