Human Face Recognition Using Third-Order Synthetic Neural Networks
Human Face Recognition Using Third-Order Synthetic Neural Networks
Japanese Face Emotions Classification Using LIP Features
GMAI '07 Proceedings of the Geometric Modelling and Imaging
Emotion recognition from facial expressions and its control using fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Voice and Facial Expression Based Classification of Emotion Using Linear Support Vector Machine
DESE '09 Proceedings of the 2009 Second International Conference on Developments in eSystems Engineering
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This paper proposes a new technique for emotion recognition of an unknown subject using General Type-2 Fuzzy sets (GT2FS). The proposed technique includes two steps- first, a type-2 fuzzy face-space is created with the background knowledge of facial features of different subjects containing different emotions. Second, the emotion of an unknown facial expression is determined based on the consensus of the measured facial features with the fuzzy face-space. The GT2FS has been used here to model the fuzzy face space. The general type-2 fuzzy involves both primary and secondary membership distributions which have been obtained here by formulating and solving an optimization problem. The optimization problem here attempts to minimize the difference between two decoded signals: the first one being the type-1 defuzzification of the average primary membership distributions obtained from n-subjects, while the second one refers to the type-2 defuzzified signal for a given primary distribution with secondary memberships as unknown. The uncertainty management policy adopted using general type-2 fuzzy set has resulted in a classification accuracy of 96.67%.