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Local generic representation for patch uLBP-based face recognition with single training sample per subject

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Abstract

In this paper, we propose a novel paradigm of Patch uniform Local Binary Patterns (PuLBP) based Local Generic Representation (LGR) for face recognition. Indeed, we introduce a new block in which an uLBP is used to approximate both reference and variation subsets. Thus, we concentrate on the challenging problem of a single sample per person in a gallery set. Particularly, the main problem is whether only one training subject per class is available. One of the novelties of our technique is to generate virtual samples of each subject. The new sample generic image in a gallery set is adopted to produce the intra-personal variations of different individuals. We illustrate the experimental results of our new algorithm on different benchmark databases, including the AR face database, the Extended Yale B face database, the FRGC database and the FEI database.

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Acknowledgements

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program. D. L acknowledges partial support by NSF DMS 1005799 and DMS 1008900.

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Correspondence to Taher Khadhraoui.

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Khadhraoui, T., Borgi, M.A., Benzarti, F. et al. Local generic representation for patch uLBP-based face recognition with single training sample per subject. Multimed Tools Appl 77, 24203–24222 (2018). https://doi.org/10.1007/s11042-018-5679-0

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  • DOI: https://doi.org/10.1007/s11042-018-5679-0

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