Mouth and eyebrow segmentation for emotion recognition using interpolated polynomials

Abstract

Facial Expression Recognition (FER) is a research area that has been interesting for computer science community in recent years. In this paper, we propose a methodology for the three stages of a FER system. In the pre-processing stage a method based on edge detectors and thresholding operators for eyebrow and mouth segmentation is proposed; the next stage is feature extraction, we propose using polynomials as features for describing eyebrows and mouth regions. Finally, in classification stage different supervised learners such as: Neural Networks, K-Nearest Neighbors and C4.5 decision trees are tested in order to obtain a model for classifying three out of six basic emotions (anger, happiness and surprise). According to our results, the proposed approach has acceptable accuracy for predicting new examples.