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Mathematical Problems in Engineering
Volume 2009, Article ID 802932, 16 pages
Research Article

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

1Institute of Computer Science & Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2School of Information Science and Technology, Southwest Jiaotong University, Chengdou 610031, China

Received 16 January 2009; Revised 15 April 2009; Accepted 12 May 2009

Academic Editor: Panos Liatsis

Copyright © 2009 Yong Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.