000192745 001__ 192745
000192745 005__ 20190316235806.0
000192745 037__ $$aREP_WORK
000192745 088__ $$aIdiap-RR-17-2013
000192745 245__ $$aSession Variability Modelling for Face Authentication
000192745 269__ $$a2013
000192745 260__ $$bIdiap$$c2013
000192745 336__ $$aReports
000192745 520__ $$aThis paper 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. We 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, we show that using ISV leads to significant error rate reductions of, on average, 22% on the challenging and publicly-available databases SCface, BANCA, MOBIO, and Multi-PIE. Finally, we 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.
000192745 700__ $$aMcCool, Chris
000192745 700__ $$aWallace, Roy
000192745 700__ $$aMcLaren, Mitchell
000192745 700__ $$0246037$$aEl Shafey, Laurent$$g197194
000192745 700__ $$0243994$$aMarcel, Sébastien$$g143942
000192745 8564_ $$s2613034$$uhttps://infoscience.epfl.ch/record/192745/files/McCool_Idiap-RR-17-2013.pdf$$yn/a$$zn/a
000192745 909C0 $$0252189$$pLIDIAP$$xU10381
000192745 909CO $$ooai:infoscience.tind.io:192745$$pSTI$$preport$$qGLOBAL_SET
000192745 937__ $$aEPFL-REPORT-192745
000192745 970__ $$aMcCool_Idiap-RR-17-2013/LIDIAP
000192745 973__ $$aEPFL
000192745 980__ $$aREPORT