000192522 001__ 192522
000192522 005__ 20190316235801.0
000192522 0247_ $$2doi$$a10.1049/iet-bmt.2012.0059
000192522 022__ $$a2047-4938
000192522 037__ $$aARTICLE
000192522 245__ $$aSession variability modelling for face authentication
000192522 260__ $$c2013
000192522 269__ $$a2013
000192522 336__ $$aJournal Articles
000192522 520__ $$aThis study examines session variability modelling for face authentication using Gaussian mixture models. Session variability modelling aims to explicitly model and suppress detrimental within-class (inter-session) variation. The authors examine two techniques to do this, inter-session variability modelling (ISV) and joint factor analysis (JFA), which were initially developed for speaker authentication. We present a self-contained description of these two techniques and demonstrate that they can be successfully applied to face authentication. In particular, they show that using ISV leads to significant error rate reductions of, on average, 26% on the challenging and publicly available databases SCface, BANCA, MOBIO and multi-PIE. Finally, the authors show that a limitation of both ISV and JFA for face authentication is that the session variability model captures and suppresses a significant portion of between-class variation.
000192522 700__ $$aMcCool, Chris
000192522 700__ $$aWallace, Roy
000192522 700__ $$aMcLaren, Mitchell
000192522 700__ $$0246037$$aEl Shafey, Laurent$$g197194
000192522 700__ $$0243994$$aMarcel, Sébastien$$g143942
000192522 773__ $$j2$$k3$$q117-129$$tIET Biometrics
000192522 8564_ $$uhttp://publications.idiap.ch/index.php/publications/showcite/McCool_Idiap-RR-17-2013$$zURL
000192522 909C0 $$0252189$$pLIDIAP$$xU10381
000192522 909CO $$ooai:infoscience.tind.io:192522$$pSTI$$particle$$qGLOBAL_SET
000192522 917Z8 $$x148230
000192522 937__ $$aEPFL-ARTICLE-192522
000192522 970__ $$aMcCool_IET_BMT_2013/LIDIAP
000192522 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000192522 980__ $$aARTICLE