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Non-Linear Approaches for the Classification of Facial Expressions at Varying Degrees of Intensity
Reilly, Jane; Ghent, John; McDonald, John
The research discussed in this paper documents a comparative analysis of two nonlinear dimensionality reduction techniques for the classification of facial expressions at varying degrees of intensity. These nonlinear dimensionality reduction techniques are Kernel Principal Component Analysis (KPCA) and Locally Linear Embedding (LLE). The approaches presented in this paper employ psychological tools, computer vision techniques and machine learning algorithms. In this paper we concentrate on comparing the performance of these two techniques when combined with Support Vector Machines (SVMs) at the task of classifying facial expressions across the full expression intensity range from near-neutral to extreme facial expression. Receiver Operating Characteristic (ROC) curve analysis is employed as a means of comprehensively comparing the results of these techniques.
Keyword(s): support vector machines; computer vision; curve fitting; face recognition; image classification; learning (artificial intelligence); principal component analysis
Publication Date:
2007
Type: Book chapter
Peer-Reviewed: Yes
Institution: Maynooth University
Funder(s): Science Foundation Ireland
Citation(s): Reilly, Jane and Ghent, John and McDonald, John (2007) Non-Linear Approaches for the Classification of Facial Expressions at Varying Degrees of Intensity. In: IMVIP 2007. International Machine Vision and Image Processing Conference, 2007. IEEE, pp. 125-132. ISBN 0769528872
Publisher(s): IEEE
File Format(s): other
Related Link(s): http://mural.maynoothuniversity.ie/8345/1/JM-Non-Linear-2007.pdf
First Indexed: 2020-04-02 06:30:53 Last Updated: 2020-04-02 06:30:53