ARTICLE Khoury_IMAVIS_2014/IDIAP Bi-Modal Biometric Authentication on Mobile Phones in Challenging Conditions Khoury, Elie El Shafey, Laurent McCool, Chris Günther, Manuel Marcel, Sébastien https://publications.idiap.ch/index.php/publications/showcite/Khoury_Idiap-RR-30-2013 Related documents Image and Vision Computing 1147-1160 2014 http://www.sciencedirect.com/science/article/pii/S0262885613001492 URL http://dx.doi.org/10.1016/j.imavis.2013.10.001 doi This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 6.3% and 1.9% for Female and Male trials, respectively. REPORT Khoury_Idiap-RR-30-2013/IDIAP Bi-Modal Biometric Authentication on Mobile Phones in Challenging Conditions Khoury, Elie El Shafey, Laurent McCool, Chris Günther, Manuel Marcel, Sébastien EXTERNAL https://publications.idiap.ch/attachments/reports/2013/Khoury_Idiap-RR-30-2013.pdf PUBLIC Idiap-RR-30-2013 2013 Idiap October 2013 This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 6.3% and 1.9% for Female and Male trials, respectively.