Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91684
PIRA download icon_1.1View/Download Full Text
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 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.2021.3089836
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Lin_Extended_JSSL_Multi-Feature.pdf2.95 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

99
Last Week
1
Last month
Citations as of Mar 24, 2024

Downloads

40
Citations as of Mar 24, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.