Inter-session variability modelling and joint factor analysis for face authentication

Abstract

This paper applies inter-session variability modelling and joint factor analysis to face authentication using Gaussian mixture models. These techniques, originally developed for speaker authentication, aim to explicitly model and remove detrimental within-client (inter-session) variation from client models. We apply the techniques to face authentication on the publicly-available BANCA, SCface and MOBIO databases. We propose a face authentication protocol for the challenging SCface database, and provide the first results on the MOBIO still face protocol. The techniques provide relative reductions in error rate of up to 44%, using only limited training data. On the BANCA database, our results represent a 31% reduction in error rate when benchmarked against previous work.

DOI: 10.1109/IJCB.2011.6117599

Extracted Key Phrases

10 Figures and Tables

02040201220132014201520162017
Citations per Year

85 Citations

Semantic Scholar estimates that this publication has 85 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Wallace2011IntersessionVM, title={Inter-session variability modelling and joint factor analysis for face authentication}, author={Roy Wallace and Mitchell McLaren and Chris McCool and S{\'e}bastien Marcel}, booktitle={IJCB}, year={2011} }