An improved boosting algorithm and its application to facial emotion recognition

This paper develops a regularized discriminant analysis (RDA)-based boosting algorithm, and its application of the facial emotion recognition. The RDA-based boosting algorithm uses RDA as a learning rule in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2012-03, Vol.3 (1), p.11-17
Main Authors: Lee, Chien-Cheng, Shih, Cheng-Yuan, Lai, Wen-Ping, Lin, Po-Chiang
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Classification
Computational Intelligence
Discriminant analysis
Emotion recognition
Emotions
Engineering
Entropy
Feature recognition
Feature selection
Ill posed problems
Methods
Original Research
Particle swarm optimization
Probability distribution
Random variables
Regularization
Robotics and Automation
User Interfaces and Human Computer Interaction
Wavelet transforms
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Summary:This paper develops a regularized discriminant analysis (RDA)-based boosting algorithm, and its application of the facial emotion recognition. The RDA-based boosting algorithm uses RDA as a learning rule in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses a particle swarm optimization algorithm to estimate optimal parameters in RDA. In this work, the proposed RDA-based boosting is used in the facial emotion recognition, and achieves a good performance. In the facial emotion recognition, contourlet features are extracted and followed by an entropy criterion to select the informative contourlet features which is a subset of informative and non-redundant contourlet features. Experiment results demonstrate that the proposed RDA-based boosting can accurately and robustly recognize facial emotions.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-011-0085-8