Local generic representation for patch uLBP-based face recognition with single training sample per subject
| Autor: | Hamid Amiri, Chokri Ben Amar, Mohamed Anouar Borgi, Taher Khadhraoui, Faouzi Benzarti |
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| Rok vydání: | 2018 |
| Předmět: |
Computer Networks and Communications
business.industry Local binary patterns Computer science 020207 software engineering Pattern recognition Sample (statistics) 02 engineering and technology Facial recognition system Set (abstract data type) Hardware and Architecture Face (geometry) 0202 electrical engineering electronic engineering information engineering Media Technology Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business Representation (mathematics) Software Block (data storage) |
| Zdroj: | Multimedia Tools and Applications. 77:24203-24222 |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-018-5679-0 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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