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– In this paper, a new technique called Two Dimensional Gabor Fisher Discriminant (2DGFD) is derived and implemented for image representation and recognition. In our approach, the Gabor wavelets are used to extract facial features. The Principal Component Analysis (PCA) is applied directly on the Gabor transformed matrices to remove redundant information(More)
In this paper, a new technique called two dimensional Gabor principle component analysis (2DGPCA) is derived and implemented for image representation and recognition. The 2DGPCA method addresses the problems of feature extraction, feature selection and classification. In this approach, the Gabor wavelets are used to extract facial features. The principle(More)
— this paper presents a novel image feature extraction and recognition method two dimensional linear discriminant analysis (2DLDA) in a much smaller subspace. Image representation and recognition based on the Fisher's criterion is statistically dependent on the evaluation of the covariance matrices. Since the proposed approach computes the covariance(More)
This paper presents a Complete Orthogonal Image discriminant (COID) method and its application to biometric face recognition. The novelty of the COID method comes from 1) the derivation of two kinds of image discriminant features, image regular and image irregular, in the feature extraction stage and 2) the development of the Complete OID (COID)(More)
In this paper, a novel two dimensional orthogonal wavelet features (2DOWF) method is presented for image representation and face recognition. The 2DOWF method derives 2D orthogonal wavelet (Gabor or Log Gabor) features in the feature extraction stage and then develops the cosine matrix measure for classification in the pattern recognition stage. 2DOWF(More)
This paper describes a novel algorithm, 2D-FPCA, for face feature extraction and representation. The new algorithm fuses the two dimensional Fisherface method with the two dimensional principal component analysis (2DPCA). Our algorithm operates on the two dimensional image matrices. Therefore a total image covariance matrix can be constructed directly using(More)
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