Titijaroonroj, Taravichet

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Titijaroonroj, Taravichet
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Titijaroonrog, Taravichet
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taravichet.ti@kmitl.ac.th

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    Item type:Publication,
    An Individual Local Mean-based 2DPCA for Face Recognition under Illumination Effects
    (2019-07-01)
    Hancherngchai, Kangsadan
    ;
    Titijaroonroj, Taravichet
    ;
    Rungrattanaubol, Jaratsri
    Principal component analysis (PCA) is a classical technique in pattern recognition and computer vision. It is one of the most successful techniques for face recognition. The PCA consists of two main steps including (I) covariance matrix calculation and (II) eigenvector and eigenvalue extraction. In case of face recognition, the input image is converted to the vector form before forwarding to covariance matrix computation. Then, the matrix is used to extract the eigenvector and eigenvalue. Two-dimensional PCA (2DPCA) is introduced to reduce high-dimensional problems. The illumination effect problems in the face recognition is still needed to be resolved. In order to improve and solve the problems, this paper proposes an individual local mean-based 2DPCA (ILM-2DPCA), which replaces a single local mean in 2DPCA method. The individual local mean can provide more appropriate mean to each image, which can reduce the illumination effect effectively. The experimental study is set up on dataset Yale face database B+. The results indicate that the proposed method outperforms, based on the accuracy rate, all the baseline methods which are 2DPCA, I-2DPCA, Bi2DPCA and 2D<sup>2</sup>PCA.
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    Item type:Publication,
    Regional covariance matrix-based two-dimensional PCA for face recognition
    (2020-01-01)
    Titijaroonroj, Taravichet
    ;
    Hancherngchai, Kangsadan
    ;
    Rungrattanaubol, Jaratsri
    Two-dimensional principal component analysis (2DPCA) is widely used in many applications, especially, face recognition. A key factor to improve the performance of the 2DPCA method comes from the efficiency of the covariance matrix. This paper believes that the effective eigenvector can be extracted when the effective covariance matrix is given. Therefore, the computing covariance matrix is a focus point in this paper. The set of the covariance matrix in the 2DPCA and its extensions is usually represented with a single directional correlation, which is then used to obtain a mean covariance matrix by using the average technique. This causes in obtaining the ineffective eigenvector since the covariance matrix is ineffective. In order to obtain the effective eigenvector, a regional covariance matrix-based on 2DPCA method (RCM-2DPCA) is proposed here. The contribution of this paper consists of two main parts including (i) regional matrix calculation for computing the two directional correlations and (ii) ELSSP conversion for extracting the effective representation of the covariance matrix. The experimental results show that the performance of the proposed method is higher than the baseline methods including 2DPCA, I-2DPCA, Bi2DPCA, 2D2PCA and ILM-2DPCA methods on a basis of three well-known datasets-ORL Face, Yale Face, and Yale Face extended B+ datasets.