Designing a Dual-level Facial Expression Evaluation System for Performers Using Geometric Features and Petri Nets

Abstract

The existing methods of Facial Expression Recognition (FER) primarily analyze six basic expressions, namely, surprise, happiness, anger, sadness, fear, and disgust. The Indian performing arts use three more well-defined expressions—peaceful, proud, and erotic. This study proposes an intelligent dual-level expression evaluation system that classifies performance-specific expressions into nine classes, assigns intensity level to the expression, and suggests modifications to the user for precise exhibition of an expression. At decision level-1 of a dual-level system, an 11-state model is designed to classify the nine expressions. The model is verified using the Colored Petri Net that helps analyze the rules used for the classification. Decision level-1 is also implemented using input feature database and SVM classifier, which yields 95.77% accuracy. Further, at decision level-2, SVM is used to assign an intensity level to the correctly classified images. In case of incorrectly exhibited expressions, feedback is provided to the user about the incorrect facial component state. The application-specific image dataset is used for the present study. The qualitative comparison with the other FER approaches is also carried out. With the increasing popularity of Indian classical dance in Western and Asian countries, the dual-level system enables learners of performing arts to practice, evaluate, and improvise their expression skills.