Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition

Ding, C; Choi, J; Tao, D; Davis, LS

Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition

Ding, C Choi, J Tao, D

Davis, LS

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38 (3), pp. 518 - 531
Issue Date:: 2016-03-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (2.9 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ding, C	en_US
dc.contributor.author	Choi, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Davis, LS	en_US
dc.date.issued	2016-03-01	en_US
dc.identifier.citation	IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38 (3), pp. 518 - 531	en_US
dc.identifier.issn	0162-8828	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43295
dc.description.abstract	© 1979-2012 IEEE. To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract 'Multi-Directional Multi-Level Dual-Cross Patterns' (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140102164
dc.relation	http://purl.org/au-research/grants/arc/FT130101457
dc.relation.ispartof	IEEE Transactions on Pattern Analysis and Machine Intelligence	en_US
dc.relation.isbasedon	10.1109/TPAMI.2015.2462338	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Biometric Identification	en_US
dc.subject.mesh	Face	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Image Processing, Computer-Assisted	en_US
dc.subject.mesh	Principal Component Analysis	en_US
dc.title	Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	38	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

Abstract:

© 1979-2012 IEEE. To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract 'Multi-Directional Multi-Level Dual-Cross Patterns' (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.

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

http://hdl.handle.net/10453/43295