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    Monogenic Riesz wavelet representation for micro-expression recognition

    Access Status
    Fulltext not available
    Authors
    Oh, Y.
    Le Ngo, A.
    See, J.
    Liong, S.
    Phan, R.
    Ling, Huo Chong
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Oh, Y. and Le Ngo, A. and See, J. and Liong, S. and Phan, R. and Ling, H.C. 2015. Monogenic Riesz wavelet representation for micro-expression recognition, pp. 1237-1241.
    Source Title
    International Conference on Digital Signal Processing, DSP
    DOI
    10.1109/ICDSP.2015.7252078
    ISBN
    9781479980581
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/71009
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 IEEE. A monogenic signal is a two-dimensional analytical signal that provides the local information of magnitude, phase, and orientation. While it has been applied on the field of face and expression recognition [1], [2], [3], there are no known usages for subtle facial micro-expressions. In this paper, we propose a feature representation method which succinctly captures these three low-level components at multiple scales. Riesz wavelet transform is employed to obtain multi-scale monogenic wavelets, which are formulated by quaternion representation. Instead of summing up the multi-scale monogenic representations, we consider all monogenic representations across multiple scales as individual features. For classification, two schemes were applied to integrate these multiple feature representations: a fusion-based method which combines the features efficiently and discriminately using the ultra-fast, optimized Multiple Kernel Learning (UFO-MKL) algorithm; and concatenation-based method where the features are combined into a single feature vector and classified by a linear SVM. Experiments carried out on a recent spontaneous micro-expression database demonstrated the capability of the proposed method in outperforming the state-of-the-art monogenic signal approach to solving the micro-expression recognition problem.

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