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The image decomposition based method is one of the efficient and important face recognition solutions for the single sample per person problem. The low image decomposition performance and the unconvincing reconstruction of the approximation image are the two main limitations of the previous methods. In this paper, a new single sample face recognition method(More)
Fisher linear discriminant analysis (FLDA) algorithm is a popular subspace method for face recognition, which requires that the number of training samples for each object is not less than two. In this paper, an adaptive approximation image reconstruction method based on economy singular value decomposition (ESVD) algorithm is proposed for the single sample(More)
The extended sparse representation classifier (ESRC) is one of the state-of-the-art solutions for single sample face recognition, but it performs unsatisfactorily under varying illumination. There are two main reasons: one is that the mutual influences of the reflectance and illumination in intra-class variant bases of the ESRC are extreme under varying(More)
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