Face Recognition with Single Training Sample per Subject

This paper presents an approach called Patch uniform Local Binary Patterns (PuLBP) based Local Generic Representation (LGR) for face recognition. In fact, we insert a novel block that comports a uLBP in order to approximate both variation and reference subsets. Consequently, the focus will be on the difficult problem of a unique sample by person in a gallery set. More specifically, the major problem is if having solely one training person in every class is possible. The generation of virtual samples of every sample is one of the innovations of our technique. In a gallery set, each sample is used to generate the intra-personal variety of distinct individuals. We demonstrate the experimental results of our novel algorithm on many reference databases that include the FRGCv1, AR, the Georgia Tech (GT), the FEI and the Extended Cohn-Kanade.

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