Computing with words model for emotion recognition by facial expression analysis using interval type-2 fuzzy sets

The paper provides a novel approach to emotion recognition of subjects from the user-specified word description of their facial features. The problem is solved in two phases. In the first phase, an interval type-2 fuzzy membership space for each facial feature in different linguistic grades for different emotions is created. In the second phase, a set of fuzzy emotion-classifier rules is instantiated with fuzzy word description about facial features to infer the winning emotion class. The most attractive part of this research is to autonomously transform user specified word descriptions into membership functions and construction of footprint of uncertainty for each facial feature in different linguistic grades. The proposed technique for emotion classification is very robust as it is sensitive to changes in word description only rather than the absolute measurement of features. Besides it offers a good classification accuracy of 87.8% and thus comparable with existing techniques.