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Mahmoud Khademi

  1. Extended Active Learning Method.

    Пт, 11/12/2010 - 16:01 — SomNambul
    Authors: Mahmoud Khademi, Ali Akbar Kiaei, Saeed Bagheri Shouraki, Seyed Hossein Khasteh
    Subjects: Artificial Intelligence
    Abstract

    Active Learning Method (ALM) is a soft computing method which is used for
    modeling and controlling, based on fuzzy logic. Although it has shown that it
    acts well in dynamic environments, its operators can't support it very well in
    complex situations, because of losing data. So ALM could find better membership
    functions, if the operators were replaced with ones that fit to what ALM wants.
    This paper replaced two new operators instead of its original ones; therefore,
    finding membership functions is renewed and it's found in better way.

  2. Facial Expression Representation Using Heteroscedastic Linear Discriminant Analysis and Gabor Wavelets.

    Втр, 04/13/2010 - 15:02 — SomNambul
    Authors: Mahmoud Khademi, Mehran Safayani, Mohammad H. Kiapour, Mohammad T. Manzuri
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    In this paper, a novel representation for facial expressions in
    two-dimensional image sequences is presented. We apply a variation of
    two-dimensional heteroscedastic linear discriminant analysis (2DHLDA)
    algorithm, as an efficient dimensionality reduction technique, to Gabor
    representation of the input sequence. 2DHLDA is an extension of the
    two-dimensional linear discriminant analysis (2DLDA) approach and removes the
    equal within-class covariance. By applying 2DHLDA in two directions, we
    eliminate the correlations between both image columns and image rows.

  3. Extended Two-Dimensional PCA for Efficient Face Representation and Recognition.

    Ср, 04/07/2010 - 15:02 — SomNambul
    Authors: Mahmoud Khademi, Mehran Safayani, Mohammad T. Manzuri-Shalmani
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    In this paper a novel method called Extended Two-Dimensional PCA (E2DPCA) is
    proposed which is an extension to the original 2DPCA. We state that the
    covariance matrix of 2DPCA is equivalent to the average of the main diagonal of
    the covariance matrix of PCA. This implies that 2DPCA eliminates some
    covariance information that can be useful for recognition. E2DPCA instead of
    just using the main diagonal considers a radius of r diagonals around it and
    expands the averaging so as to include the covariance information within those
    diagonals. The parameter r unifies PCA and 2DPCA.

  4. Analysis, Interpretation, and Recognition of Facial Action Units and Expressions Using Neuro-Fuzzy Modeling.

    Втр, 04/06/2010 - 15:01 — SomNambul
    Authors: Mahmoud Khademi, Mohammad T. Manzuri-Shalmani, Ali A. Kiaei, Mohammad Hadi Kiapour
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    In this paper an accurate real-time sequence-based system for representation,
    recognition, interpretation, and analysis of the facial action units (AUs) and
    expressions is presented.

  5. Recognizing Combinations of Facial Action Units with Different Intensity Using a Mixture of Hidden Markov Models and Neural Network.

    Втр, 04/06/2010 - 15:01 — SomNambul
    Authors: Mahmoud Khademi, Mohammad T. Manzuri-Shalmani, Mohammad H. Kiapour, Ali A. Kiaei
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    Facial Action Coding System consists of 44 action units (AUs) and more than
    7000 combinations. Hidden Markov models (HMMs) classifier has been used
    successfully to recognize facial action units (AUs) and expressions due to its
    ability to deal with AU dynamics. However, a separate HMM is necessary for each
    single AU and each AU combination. Since combinations of AU numbering in
    thousands, a more efficient method will be needed. In this paper an accurate
    real-time sequence-based system for representation and recognition of facial
    AUs is presented.

  6. Multilinear Biased Discriminant Analysis: A Novel Method for Facial Action Unit Representation.

    Втр, 04/06/2010 - 15:01 — SomNambul
    Authors: Mahmoud Khademi, Mehran Safayani, Mohammad T. Manzuri-Shalmani
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    In this paper a novel efficient method for representation of facial action
    units by encoding an image sequence as a fourth-order tensor is presented. The
    multilinear tensor-based extension of the biased discriminant analysis (BDA)
    algorithm, called multilinear biased discriminant analysis (MBDA), is first
    proposed. Then, we apply the MBDA and two-dimensional BDA (2DBDA) algorithms,
    as the dimensionality reduction techniques, to Gabor representations and the
    geometric features of the input image sequence respectively.

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