Self-Learning Facial Emotional Feature Selection Based on Rough Set Theory

Hao Kong,Yong Yang,Guoyin Wang

doi:10.1155/2009/802932

Abstract

Emotion recognition is very important for human-computer intelligent interaction. It is generally performed on facial or audio information by artificial neural network, fuzzy set, support vector machine, hidden Markov model, and so forth. Although some progress has already been made in emotion recognition, several unsolved issues still exist. For example, it is still an open problem which features are the most important for emotion recognition. It is a subject that was seldom studied in computer science. However, related research works have been conducted in cognitive psychology. In this paper, feature selection for facial emotion recognition is studied based on rough set theory. A self-learning attribute reduction algorithm is proposed based on rough set and domain oriented data-driven data mining theory. Experimental results show that important and useful features for emotion recognition can be identified by the proposed method with a high recognition rate. It is found that the features concerning mouth are the most important ones in geometrical features for facial emotion recognition.

Highlights

In recent years, there has been a growing interest in improving all aspects of the interactions between humans and computers
Feature selection for facial emotion recognition is studied based on rough set theory
It is found that the features concerning mouth are the most important ones in geometrical features for facial emotion recognition

Summary

Introduction

There has been a growing interest in improving all aspects of the interactions between humans and computers. There have been several research works related to the important features for emotion in cognitive psychology. In our previous works of emotion recognition in 7–10 , attribute reduction algorithms based on classical rough set are used for the purpose of facial emotional feature selection, and SVM is taken as the classifiers. These research works can avoid the discretization process, the parameters in these methods should be given according to prior experience of domain experts, for example, the fuzzy set membership function in Jensen’s fuzzy-rough attribute reduction algorithm, the population amount for Shang’s method. A novel feature selection method based on tolerance relation is proposed, which can avoid the process of discretization.

Basic Concepts of Rough Set Theory

Attribute Reduction for Emotion Recognition

Experiment Results and Discussion

Experiments For SARA as a Feature Selection Method for Emotion Recognition

Experiments for the Features Concerning Mouth for Emotion Recognition

Conclusion