Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91684
Title: Extended JSSL for multi-feature face recognition via intra-class variant dictionary
Authors: Lin, GJ
Zhang, QR
Zhou, SY
Jiang, XG
Wu, H
You, HR
Li, ZX
He, P
Li, H 
Issue Date: 2021
Source: IEEE access, 2021, v. 9, p. 91807-91819
Abstract: This 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.
Keywords: Face recognition
Feature extraction
Training
Testing
Dictionaries
Data mining
Collaboration
Sparse representation
Image classification
Multi-feature
Face recognition
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3089836
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
The following publication G. Lin, Q. Zhang, S. Zhou, X. Jiang, H. Wu, H. You, Z. Li, P. He, H. Li, Extended JSSL for Multi-Feature Face Recognition via Intra-Class Variant Dictionary, IEEE Access, 2021 is available at 10.1109/ACCESS.2021.3089836
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