Sparse representation based undersampled face recognition with shared prototype–auxiliary dictionaries
| Autor: | Xiao Ma, Jufu Feng, Wenjing Zhuang, Yuelong Li |
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| Rok vydání: | 2017 |
| Předmět: |
TheoryofComputation_MISCELLANEOUS
0209 industrial biotechnology K-SVD Computer science business.industry Cognitive Neuroscience Pattern recognition Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Variation (game tree) Sparse approximation Facial recognition system Computer Science Applications Data set 020901 industrial engineering & automation Artificial Intelligence Component (UML) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Dictionary learning Subspace topology |
| Zdroj: | Neurocomputing. 239:58-68 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2017.01.082 |
| Popis: | The sparse representation with auxiliary dictionary based face recognition methods have achieved significant performance in recent years. The prevailing auxiliary dictionary based methods use training dictionary and auxiliary dictionary to separate facial samples prototype dictionary and the intra-class variation component respectively. While in undersampled cases, training dictionary usually contains large intra-class variations, the prototype component cannot be fully separated. For this limitation, a sparse representation based classification with shared prototypeauxiliary dictionaries (SRSPA) method is proposed. In SRSPA, a shared prototype dictionary is exploited to specifically separating the prototype component. In addition, a novel dictionary learning method is proposed, which fully considers the separation ability of prototype dictionary and auxiliary dictionary. Experiments on various data sets verify efficacy of the proposed SRSPA especially in undersampled cases. |
| Databáze: | OpenAIRE |
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