access icon free Face recognition algorithm based on feature descriptor and weighted linear sparse representation

Generally, the commonly used sparse-based methods, such as sparse representation classifier, have achieved a good recognition result in face recognition. However, there exist several problems in those methods. First, those methods think that the importance of each atom is the same in representing other query samples. This is not reasonable because different atoms contain different amounts of information, their importance should be different when they together represent the query samples. Second, those methods cannot meet the real-time requirement when dealing with large data set. In this study, on the one hand, the authors propose a fast extended sparse-weighted representation classifier (FESWRC) by considering the different importance of atoms and using primal augmented Lagrangian method as well as principal component analysis. On the other hand, the authors propose a distinctive feature descriptor, named logarithmic-weighted sum (LWS) feature descriptor. The authors combine FESWRC and LWS and used for face recognition, this method is called face recognition algorithm based on feature descriptor and weighted linear sparse representation (FDWLSR). Experimental results show that FDWLSR can realise real-time recognition and the recognition rate can achieve 100.0, 100.0, 91.6, 93.4 and 87.4%, respectively, on the Yale, Olivetti Research Laboratory (ORL), faculdade de engenharia industrial (FEI), face recognition technology program (FERET) and labelled face in the wild datasets.

Inspec keywords: image representation; face recognition; feature extraction; principal component analysis

Other keywords: face recognition algorithm; recognition rate; sparse representation classifier; different importance; weighted linear sparse representation; sparse-based methods; named logarithmic-weighted sum; query samples; good recognition result; sparse-weighted representation classifier; distinctive feature descriptor; different atoms; real-time recognition

Subjects: Image recognition; Other topics in statistics; Other topics in statistics; Computer vision and image processing techniques

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