Exploring manifold structure of face images via multiple graphs

Handle URI:
http://hdl.handle.net/10754/555690
Title:
Exploring manifold structure of face images via multiple graphs
Authors:
Alghamdi, Masheal
Abstract:
Geometric structure in the data provides important information for face image recognition and classification tasks. Graph regularized non-negative matrix factorization (GrNMF) performs well in this task. However, it is sensitive to the parameters selection. Wang et al. proposed multiple graph regularized non-negative matrix factorization (MultiGrNMF) to solve the parameter selection problem by testing it on medical images. In this paper, we introduce the MultiGrNMF algorithm in the context of still face Image classification, and conduct a comparative study of NMF, GrNMF, and MultiGrNMF using two well-known face databases. Experimental results show that MultiGrNMF outperforms NMF and GrNMF for most cases.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Alghamdi, Masheal. "Exploring manifold structure of face images via multiple graphs." In Sixth International Conference on Machine Vision (ICMV 13), pp. 90671D-90671D. International Society for Optics and Photonics, 2013
Publisher:
SPIE-Intl Soc Optical Eng
Journal:
Sixth International Conference on Machine Vision (ICMV 2013)
Conference/Event name:
6th International Conference on Machine Vision, ICMV 2013
Issue Date:
24-Dec-2013
DOI:
10.1117/12.2051527
Type:
Conference Paper
Additional Links:
http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2051527
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlghamdi, Mashealen
dc.date.accessioned2015-05-25T14:45:17Zen
dc.date.available2015-05-25T14:45:17Zen
dc.date.issued2013-12-24en
dc.identifier.citationAlghamdi, Masheal. "Exploring manifold structure of face images via multiple graphs." In Sixth International Conference on Machine Vision (ICMV 13), pp. 90671D-90671D. International Society for Optics and Photonics, 2013en
dc.identifier.doi10.1117/12.2051527en
dc.identifier.urihttp://hdl.handle.net/10754/555690en
dc.description.abstractGeometric structure in the data provides important information for face image recognition and classification tasks. Graph regularized non-negative matrix factorization (GrNMF) performs well in this task. However, it is sensitive to the parameters selection. Wang et al. proposed multiple graph regularized non-negative matrix factorization (MultiGrNMF) to solve the parameter selection problem by testing it on medical images. In this paper, we introduce the MultiGrNMF algorithm in the context of still face Image classification, and conduct a comparative study of NMF, GrNMF, and MultiGrNMF using two well-known face databases. Experimental results show that MultiGrNMF outperforms NMF and GrNMF for most cases.en
dc.publisherSPIE-Intl Soc Optical Engen
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2051527en
dc.rightsArchived with thanks to Proceedings of SPIEen
dc.titleExploring manifold structure of face images via multiple graphsen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalSixth International Conference on Machine Vision (ICMV 2013)en
dc.conference.date2013-11-16 to 2013-11-17en
dc.conference.name6th International Conference on Machine Vision, ICMV 2013en
dc.conference.locationLondon, GBRen
dc.eprint.versionPublisher's Version/PDFen
kaust.authorAlghamdi, Masheal M.en
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