%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 12:51:25 PM

@INPROCEEDINGS{Poh:2002:nnsp,
         author = {Poh, Norman and Bengio, Samy and Korczak, Jerzy},
       projects = {Idiap},
          title = {A Multi-sample Multi-source Model for Biometric Authentication},
      booktitle = {{IEEE} International Workshop on Neural Networks for Signal Processing (NNSP)},
           year = {2002},
       crossref = {poh02},
       abstract = {In this study, two techniques that can improve the authentication process are examined: (i) multiple samples and (ii) multiple biometric sources. We propose the fusion of multiple samples obtained from multiple biometric sources at the score level. By using the average operator, both the theoretical and empirical results show that integrating as many samples and as many biometric sources as possible can improve the overall reliability of the system. This strategy is called multi-sample multi-source approach. This strategy was tested on a real-life database using neural networks trained in one-versus-all configuration.},
            pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-14.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-14.ps.gz},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{Poh02,
         author = {Poh, Norman and Bengio, Samy and Korczak, Jerzy},
       projects = {Idiap},
          title = {A Multi-sample Multi-source Model for Biometric Authentication},
           type = {Idiap-RR},
         number = {Idiap-RR-14-2002},
           year = {2002},
    institution = {IDIAP},
       abstract = {In this study, two techniques that can improve the authentication process are examined: (i) multiple samples and (ii) multiple biometric sources. We propose the fusion of multiple samples obtained from multiple biometric sources at the score level. By using the average operator, both the theoretical and empirical results show that integrating as many samples and as many biometric sources as possible can improve the overall reliability of the system. This strategy is called multi-sample multi-source approach. This strategy was tested on a real-life database using neural networks trained in one-versus-all configuration.},
            pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-14.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-14.ps.gz},
ipdmembership={learning},
}