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    A novel facial expression recognition based on the curevlet features

    151219_151219.pdf (650.6Kb)
    Access Status
    Open access
    Authors
    Zhou, Juxiang
    Wang, Yun-qiong
    Xu, Tianwei
    Liu, Wan-quan
    Date
    2010
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Zhou, J. and Wang, Y. and Xu, T. and Liu, Wan-quan. 2010. A novel facial expression recognition based on the curevlet features, in Chan, K.L., Sugimoto, A. & Lu, H. (ed), PSIVT 2010 Fourth Pacific-Rim symposium on image and video technology, Nov 14 2010, pp. 82-87. Singapore: IEEE Computer Society.
    Source Title
    Proceedings of the fourth Pacific-Rim symposium on image and video technology
    Source Conference
    PSIVT 2010 Fourth Pacific-Rim symposium on image and video technology
    ISBN
    9780769542850
    School
    Department of Computing
    Remarks

    Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/23208
    Collection
    • Curtin Research Publications
    Abstract

    Curvelet transform has been recently proved to be a powerful tool for multi-resolution analysis on images. In this paper we propose a new approach for facial expression recognition based on features extracted via curvelet transform. First curvelet transform is presented and its advantages in image analysis are described. Then the coefficients of curvelet in selected scales and angles are used as features for image analysis. Consequently the Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are used to reduce and optimize the curvelet features. Finally we use the nearest neighbor classifier to recognize the facial expressions based on these features. The experimental results on JAFFE and Cohn Kanade two benchmark databases show that the proposed approach outperforms the PCA and LDA techniques on the original image pixel values as well as its counterparts with the wavelet features.

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