ARTICLE McCool_IET_BMT_2013/IDIAP Session variability modelling for face authentication McCool, Chris Wallace, Roy McLaren, Mitchell El Shafey, Laurent Marcel, Sébastien https://publications.idiap.ch/index.php/publications/showcite/McCool_Idiap-RR-17-2013 Related documents IET Biometrics 2 3 117-129 2047-4938 2013 10.1049/iet-bmt.2012.0059 doi This 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. REPORT McCool_Idiap-RR-17-2013/IDIAP Session Variability Modelling for Face Authentication McCool, Chris Wallace, Roy McLaren, Mitchell El Shafey, Laurent Marcel, Sébastien EXTERNAL https://publications.idiap.ch/attachments/reports/2012/McCool_Idiap-RR-17-2013.pdf PUBLIC Idiap-RR-17-2013 2013 Idiap May 2013 This 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.