Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91684
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dc.contributorDepartment of Building and Real Estate-
dc.creatorLin, GJ-
dc.creatorZhang, QR-
dc.creatorZhou, SY-
dc.creatorJiang, XG-
dc.creatorWu, H-
dc.creatorYou, HR-
dc.creatorLi, ZX-
dc.creatorHe, P-
dc.creatorLi, H-
dc.date.accessioned2021-11-24T06:07:43Z-
dc.date.available2021-11-24T06:07:43Z-
dc.identifier.urihttp://hdl.handle.net/10397/91684-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe following publication Lin, G., Zhang, Q., Zhou, S., Jiang, X., Wu, H., You, H., ... & Li, H. (2021). Extended JSSL for Multi-Feature Face Recognition via Intra-Class Variant Dictionary. IEEE Access, 9, 91807-91819 is available at https://doi.org/10.1109/ACCESS.2021.3089836en_US
dc.subjectFace recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectTrainingen_US
dc.subjectTestingen_US
dc.subjectDictionariesen_US
dc.subjectData miningen_US
dc.subjectCollaborationen_US
dc.subjectSparse representationen_US
dc.subjectImage classificationen_US
dc.subjectMulti-featureen_US
dc.subjectFace recognitionen_US
dc.titleExtended JSSL for multi-feature face recognition via intra-class variant dictionaryen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage91807-
dc.identifier.epage91819-
dc.identifier.volume9-
dc.identifier.doi10.1109/ACCESS.2021.3089836-
dcterms.abstractThis paper focuses on how to represent the testing face images for multi-feature face recognition. The choice of feature is critical for face recognition. The different features of the sample contribute differently to face recognition. The joint similar and specific learning (JSSL) has been effectively applied in multi-feature face recognition. In the JSSL, although the representation coefficient is divided into the similar coefficient and the specific coefficient, there is the disadvantage that the training images cannot represent the testing images well, because there are probable expressions, illuminations and disguises in the testing images. We think that the intra-class variations of one person can be linearly represented by those of other people. In order to solve well the disadvantage of JSSL, in the paper, we extend JSSL and propose the extended joint similar and specific learning (EJSSL) for multi-feature face recognition. EJSSL constructs the intra-class variant dictionary to represent the probable variation between the training images and the testing images. EJSSL uses the training images and the intra-class variant dictionary to effectively represent the testing images. The proposed EJSSL method is perfectly experimented on some available face databases, and its performance is superior to many current face recognition methods.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 2021, v. 9, p. 91807-91819-
dcterms.isPartOfIEEE access-
dcterms.issued2021-
dc.identifier.isiWOS:000673705400001-
dc.identifier.eissn2169-3536-
dc.description.validate202111 bchy-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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