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 [BibTeX] [Marc21]
Session variability modelling for face authentication
Type of publication: Journal paper
Citation: McCool_IET_BMT_2013
Publication status: Published
Journal: IET Biometrics
Volume: 2
Number: 3
Year: 2013
Month: September
Pages: 117-129
ISSN: 2047-4938
Crossref: McCool_Idiap-RR-17-2013:
DOI: 10.1049/iet-bmt.2012.0059
Abstract: 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.
Projects BBfor2
Authors McCool, Chris
Wallace, Roy
McLaren, Mitchell
El Shafey, Laurent
Marcel, S├ębastien
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Total mark: 0
  • McCool_IET_BMT_2013.pdf