Title

Design of "Personalized" Classifier Using Soft Computing Techniques for "Personalized" Facial Expression Recognition

Authors

Authors

D. J. Kim;Z. Bien

Comments

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Abbreviated Journal Title

IEEE Trans. Fuzzy Syst.

Keywords

Facial expression recognition; feature selection (FS); model; building/modification (MBM); personalization; soft computing technique; FEATURE-SELECTION; NEURAL-NETWORKS; EMOTION; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

Abstract

We propose a design method of personalized classifier with soft computing techniques for automatic facial expression recognition. Motivated by the fact that even though human facial expressions of emotion are often ambiguous and inconsistent, humans are, in general, very good at classifying such complex images. In consideration of individual characteristics, we adopt a similar strategy of building a personalized classifier to enhance the recognition performance. For realization, we use a soft computing technique of neurofuzzy approach. Specifically, two core steps-"model building/modification" and "feature selection"-are applied to build a "personalized" classification structure. The proposed scheme of classifier construction achieves a higher classification rate, minimal network parameters, easy-to-extend structure, and faster computation time, among others. Four sets of facial expression data are chosen and image features are extracted from each of them to show effectiveness of the proposed method, which confirms considerable enhancement of the whole performance.

Journal Title

Ieee Transactions on Fuzzy Systems

Volume

16

Issue/Number

4

Publication Date

1-1-2008

Document Type

Article

Language

English

First Page

874

Last Page

885

WOS Identifier

WOS:000263375000005

ISSN

1063-6706

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