%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{marcel:2002:cost,
         author = {Marcel, S{\'{e}}bastien and Marcel, Christine and Bengio, Samy},
       projects = {Idiap},
          title = {A State-of-the-art Neural Network for Robust Face Verification},
      booktitle = {Proceedings of the COST275 Workshop on The Advent of Biometrics on the Internet},
           year = {2002},
        address = {Rome, Italy},
       crossref = {marcel02-36irr},
            pdf = {https://publications.idiap.ch/attachments/reports/2002/marcel_2002_cost.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2002/marcel_2002_cost.ps.gz},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{Marcel02-36IRR,
         author = {Marcel, S{\'{e}}bastien and Marcel, Christine and Bengio, Samy},
       projects = {Idiap},
          title = {A State-of-the-art Neural Network for Robust Face Verification},
           type = {Idiap-RR},
         number = {Idiap-RR-36-2002},
           year = {2002},
    institution = {IDIAP},
           note = {Published in the Proceedings of the COST275 Workshop on The Advent of Biometrics on the Internet, Rome, Italy, 7-8 November, 2002},
       abstract = {The performance of face verification systems has steadily improved over the last few years, mainly focusing on models rather than on feature processing. State-of-the-art methods often use the gray-scale face image as input. In this paper, we propose to use an additional feature to the face image: the skin color. The new feature set is tested on a benchmark database, namely XM2VTS, using a simple discriminant artificial neural network. Results show that the skin color information improves the performance and that the proposed model achieves robust state-of-the-art results.},
            pdf = {https://publications.idiap.ch/attachments/reports/2002/rr02-36.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr02-36.ps.gz},
ipdinar={2002},
ipdmembership={learning, vision},
language={English},
}