Feature selection in audiovisual emotion recognition based on rough set theory

Authors:
Yong Yang;Guoyin Wang;Peijun Chen;Jian Zhou;Kun He
Affiliations:
School of Information Science and Technology, Southwest Jiaotong University, Chengdou, P.R. China and Institute of Computer Science & Technology, Chongqing University of Posts and Telecommunicatio ...;Institute of Computer Science & Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China;School of Information Science and Technology, Southwest Jiaotong University, Chengdou, P.R. China;School of Information Science and Technology, Southwest Jiaotong University, Chengdou, P.R. China;Institute of Computer Science & Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China
Venue:
Transactions on rough sets VII
Year:
2007

Citing 16
Cited 3

Rough classification

International Journal of Man-Machine Studies
Learning Boolean concepts in the presence of many irrelevant features

Artificial Intelligence
Affective computing

Affective computing
Decision algorithms: a survey of rough set-theoretic methods

Fundamenta Informaticae - Special issue: intelligent information systems
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Using Rough Sets with Heuristics for Feature Selection

Journal of Intelligent Information Systems
Preface

Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Rough set methods in feature selection and recognition

Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Active Appearance Models

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Feature Selection Using Rough Sets Theory

ECML '93 Proceedings of the European Conference on Machine Learning
Bimodal Emotion Recognition

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Affective computing: challenges

International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Learning maximal generalized decision rules via discretization, generalization and rough set feature selection

ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Illumination Normalization for Robust Face Recognition Against Varying Lighting Conditions

AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
An Emotion Recognition System Based on Rough Set Theory

Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Facial expression recognition based on rough set theory and SVM

RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology

A Data Driven Emotion Recognition Method Based on Rough Set Theory

RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Knowledge Reduction of Covering Approximation Space

Transactions on Computational Science V
Research on rough set theory and applications in China

Transactions on rough sets VIII

Quantified Score

Hi-index	0.00

Visualization

Abstract

Affective computing is becoming an important research area in intelligent computing technology. Furthermore, emotion recognition is one of the hot topics in affective computing. It is usually studied based on facial and audio information with technologies such as ANN, fuzzy set, SVM, HMM, etc. Many different facial and acoustic features are considered in emotion recognition by researchers. The question which features are important for emotion recognition is discussed in this paper. Rough set based reduction algorithms are taken as a method for feature selection in a proposed emotion recognition system. Our simulation experiment results show that rough set theory is effective in emotion recognition. Some useful features for audiovisual emotion recognition are discovered.