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Local generic representation for patch uLBP-based face recognition with single training sample per subject
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DOI：10.1007/s11042-018-5679-0.png)
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Abstract
En 中文
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.
Keywords:
Face recognition
Uniform local binary patterns
Local generic representation
Single training sample per subject
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