%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{Khoury_IMAVIS_2014,
         author = {Khoury, Elie and El Shafey, Laurent and McCool, Chris and G{\"{u}}nther, Manuel and Marcel, S{\'{e}}bastien},
       projects = {Idiap, SNSF-LOBI, BBfor2, BEAT},
          title = {Bi-Modal Biometric Authentication on Mobile Phones in Challenging Conditions},
        journal = {Image and Vision Computing},
           year = {2014},
          pages = {1147-1160},
            url = {http://www.sciencedirect.com/science/article/pii/S0262885613001492},
            doi = {http://dx.doi.org/10.1016/j.imavis.2013.10.001},
       crossref = {Khoury_Idiap-RR-30-2013},
       abstract = {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.}
}



crossreferenced publications: 
@TECHREPORT{Khoury_Idiap-RR-30-2013,
         author = {Khoury, Elie and El Shafey, Laurent and McCool, Chris and G{\"{u}}nther, Manuel and Marcel, S{\'{e}}bastien},
       projects = {Idiap, SNSF-LOBI, BBfor2, BEAT},
          month = {10},
          title = {Bi-Modal Biometric Authentication on Mobile Phones in Challenging Conditions},
           type = {Idiap-RR},
         number = {Idiap-RR-30-2013},
           year = {2013},
    institution = {Idiap},
       abstract = {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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2013/Khoury_Idiap-RR-30-2013.pdf}
}