%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{cardinaux03_avbpa,
         author = {Cardinaux, Fabien and Sanderson, Conrad and Marcel, S{\'{e}}bastien},
       projects = {Idiap},
          title = {Comparison of {MLP} and {GMM} Classifiers for Face Verification on {XM2VTS}},
      booktitle = {4th International Conference on AUDIO- and VIDEO-BASED BIOMETRIC PERSON AUTHENTICATION},
         number = {10},
           year = {2003},
        address = {University of Surrey, Guildford, UK},
       crossref = {cardinaux02rr},
       abstract = {We compare two classifier approaches, namely classifiers based on Multi Layer Perceptrons (MLPs) and Gaussian Mixture Models (GMMs,',','),
 for use in a face verification system. The comparison is carried out in terms of performance, robustness and practicability. Apart from structural differences, the two approaches use different training criteria; the MLP approach uses a discriminative criterion, while the GMM approach uses a combination of Maximum Likelihood (ML) and Maximum a Posteriori (MAP) criteria. Experiments on the XM2VTS database show that for low resolution faces the MLP approach has slightly lower error rates than the GMM approach; however, the GMM approach easily outperforms the MLP approach for high resolution faces and is significantly more robust to imperfectly located faces. The experiments also show that the computational requirements of the GMM approach can be significantly smaller than the MLP approach at a cost of small loss of performance.},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/rr03-10.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2003/rr03-10.ps.gz},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{Cardinaux02RR,
         author = {Cardinaux, Fabien and Marcel, S{\'{e}}bastien},
       projects = {Idiap},
          title = {Face Verification using {MLP} and {SVM}},
           type = {Idiap-RR},
         number = {Idiap-RR-21-2002},
           year = {2002},
    institution = {IDIAP},
       abstract = {The performance of machine learning algorithms has steadily improved over the past few years, such as MLP or more recently SVM. In this paper, we compare two successful discriminant machine learning algorithms apply to the problem of face verification: MLP and SVM. These two algorithms are tested on a benchmark database, namely XM2VTS. Results show that a MLP is better than a SVM on this particular task.},
            pdf = {https://publications.idiap.ch/attachments/reports/2002/rr-02-21.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2002/rr-02-21.ps.gz},
ipdmembership={vision},
language={English},
}