Robust Sparse Learning Based on Kernel Non-Second Order Minimization

Zhang, M; Gao, Y; Sun, C; Blumenstein, M

Robust Sparse Learning Based on Kernel Non-Second Order Minimization

Zhang, M Gao, Y Sun, C Blumenstein, M

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2019, 2019-September, pp. 2045-2049
Issue Date:: 2019-09-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 1 Sep 2021

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (391.2 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, M
dc.contributor.author	Gao, Y
dc.contributor.author	Sun, C
dc.contributor.author	Blumenstein, M
dc.date	2019-09-22
dc.date.accessioned	2020-05-29T21:52:11Z
dc.date.available	2020-05-29T21:52:11Z
dc.date.issued	2019-09-01
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2019, 2019-September, pp. 2045-2049
dc.identifier.isbn	9781538662496
dc.identifier.issn	1522-4880
dc.identifier.uri	http://hdl.handle.net/10453/140996
dc.description.abstract	© 2019 IEEE. Partial occlusions in face images pose a great problem for most face recognition algorithms due to the fact that most of these algorithms mainly focus on solving a second order loss function, e.g., mean square error (MSE), which will magnify the effect from occlusion parts. In this paper, we proposed a kernel non-second order loss function for sparse representation (KNS-SR) to recognize or restore partially occluded facial images, which both take the advantages of the correntropy and the non-second order statistics measurement. The resulted framework is more accurate than the MSE-based ones in locating and eliminating outliers information. Experimental results from image reconstruction and recognition tasks on publicly available databases show that the proposed method achieves better performances compared with existing methods.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP
dc.relation.ispartof	IEEE International Conference on Image Processing
dc.relation.isbasedon	10.1109/ICIP.2019.8803198
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Robust Sparse Learning Based on Kernel Non-Second Order Minimization
dc.type	Conference Proceeding
utslib.citation.volume	2019-September
utslib.location.activity	Taipei, Taiwan
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2021-09-01T00:00:00+1000Z
dc.date.updated	2020-05-29T21:52:01Z
pubs.finish-date	2019-09-25
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-09-22
pubs.volume	2019-September
utslib.start-page	2045
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Partial occlusions in face images pose a great problem for most face recognition algorithms due to the fact that most of these algorithms mainly focus on solving a second order loss function, e.g., mean square error (MSE), which will magnify the effect from occlusion parts. In this paper, we proposed a kernel non-second order loss function for sparse representation (KNS-SR) to recognize or restore partially occluded facial images, which both take the advantages of the correntropy and the non-second order statistics measurement. The resulted framework is more accurate than the MSE-based ones in locating and eliminating outliers information. Experimental results from image reconstruction and recognition tasks on publicly available databases show that the proposed method achieves better performances compared with existing methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/140996