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Metaface block sparse Bayesian learning for face recognition
Jin, Jun-Wei; Chen, C. L. Philip; Chen, Long
2017-06-30
Conference Name32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017
Source PublicationProceedings - 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017
Pages485-489
Conference Date5 19, 2017 - 5 21, 2017
Conference PlaceHefei, China
Author of SourceInstitute of Electrical and Electronics Engineers Inc.
AbstractFace recognition is an active and challenging task in pattern recognition and computer vision application. Sparse representation based classification has been verified to be powerful for face recognition. This paper proposes the metaface block sparse bayesian learning (MBSBL) based on the framework of sparse representation. The MBS-BL combines the metaface learning and block sparse bayesian learning together to treat the face recognition problem. After obtaining a trained optimal dictionary by the method metaface learning, the representation coefficient is solved through block sparse bayesian learning with the trained dictionary. Experimental results demonstrate that the MBSBL can combine the advantages of metaface learning and block sparse bayesian learning well to achieve better recognition rates. © 2017 IEEE.
DOI10.1109/YAC.2017.7967458
Language英语
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Document TypeConference paper
CollectionUniversity of Macau
AffiliationUniversity of Macau, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jin, Jun-Wei,Chen, C. L. Philip,Chen, Long. Metaface block sparse Bayesian learning for face recognition[C]//Institute of Electrical and Electronics Engineers Inc.,2017:485-489.
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