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

    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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