%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{fasel02a-conf,
         author = {Fasel, B.},
       projects = {Idiap},
          month = {8},
          title = {Robust {F}ace {A}nalysis using {C}onvolutional Neural Networks},
      booktitle = {Proceedings of the International {C}onference on {P}attern {R}ecognition ({ICPR} 02)},
         volume = {2},
           year = {2002},
        address = {Quebec, Canada},
           note = {IDIAP-RR 01-48},
       crossref = {fasel-rr-01-48},
       abstract = {Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also robust with regard to face location changes and scale variations. This is achieved by deploying convolutional neural networks, which are either trained for facial expression recognition or face identity recognition. Combining the outputs of these networks allows us to obtain a subject dependent or personalized recognition of facial expressions.},
            pdf = {https://publications.idiap.ch/attachments/reports/2001/rr01-48.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2001/rr01-48.ps.gz},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{fasel-RR-01-48,
         author = {Fasel, B.},
       projects = {Idiap},
          title = {Robust {F}ace {A}nalysis using {C}onvolutional Neural Networks},
           type = {Idiap-RR},
         number = {Idiap-RR-48-2001},
           year = {2001},
    institution = {IDIAP},
           note = {Published in the Proceedings of the International Conference on Pattern Recognition (ICPR 2002,',','),
 Quebec, Canada, 2002},
       abstract = {Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also robust with regard to face location changes and scale variations. This is achieved by deploying convolutional neural networks, which are either trained for facial expression recognition or face identity recognition. Combining the outputs of these networks allows us to obtain a subject dependent or personalized recognition of facial expressions.},
            pdf = {https://publications.idiap.ch/attachments/reports/2001/rr01-48.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2001/rr01-48.ps.gz},
ipdmembership={vision},
}