Geometric Feature-Based Facial Emotion Recognition Using Two-Stage Fuzzy Reasoning Model

Abstract

Facial Emotion recognition is a significant requirement in machine vision society. In this sense, this paper utilizes geometric facial features and calculates displacement of feature points between expressive and neutral frames and finally applies a two-stage fuzzy reasoning model for facial emotion recognition and classification. The prototypical emotion sequence according to the Facial Action Coding System (FACS) is formed analyzing small, medium and large displacement. Furthermore geometric displacements are fuzzified and mapped onto an Action Units (AUs) by employing first-stage fuzzy reasoning model and later AUs are fuzzified and mapped onto an Emotion space by employing second-stage fuzzy relational model. The overall performance of the proposed system is evaluated on the extended Cohn-Kanade (CK+) database for classifying basic emotions like surprise, sadness, fear, anger, and happiness. The experimental results on the task of facial emotion analysis and emotion recognition are shown to outperform other existing methods available in the literature.