Dengyi Chen

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Two-dimensional principal component analysis (2D-PCA) is a fast method for face recognition. The proposed method makes use of 2D-PCA based on two dimensional Wavelet tree matrices composed of the Wavelet approximation coefficients(WTMPCA) as opposed to the traditional 2D-PCA, which is grounded on 2D matrices in the image domain. By applying the three-level(More)
The paper introduces a face recognition method using probabilistic subspaces analysis on multi-module singular value features of face images. Singular value vector of a face image is valid feature for identification. But the recognition rate is low when only one module singular value vector is used for face recognition. To improve the recognition rate, many(More)
This paper introduces a face recognition method using Fisher's linear discriminant in the Wavelet domain composed of the Wavelet approximation coefficients (WAFLD). As opposed to other approaches for face recognition, the proposed method makes use of the approximation coefficients matrices obtained by three-level Wavelet decomposition of the input image,(More)
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