%Aigaion2 BibTeX export from Idiap Publications
%Friday 03 May 2024 08:05:09 AM

@ARTICLE{I.Mantasari_IETBMT_2014,
         author = {Mandasari, Miranti I. and G{\"{u}}nther, Manuel and Wallace, Roy and Saedi, Rahim and Marcel, S{\'{e}}bastien and Van Leeuwen, David},
       keywords = {calibration performance evaluation, calibration performance metric, categorical calibration, face recognition system, Inter-session Variability Modelling, likelihood ratio interpretation, linear score transformation, linearly calibrated face recognition scores, mobile biometrics, speaker recognition field, surveillance camera face databases},
       projects = {BBfor2},
          month = feb,
          title = {Score Calibration in Face Recognition},
        journal = {IET Biometrics},
           year = {2014},
          pages = {1-11},
           issn = {2047-4938},
            url = {http://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2013.0066},
            doi = {10.1049/iet-bmt.2013.0066},
       crossref = {I.Mantasari_Idiap-RR-01-2014},
       abstract = {This paper presents an evaluation of verification and calibration performance of a face recognition system based on inter-session variability modeling. As an extension to the calibration through linear transformation of scores, categorical calibration is introduced as a way to include additional information of images to calibration. The cost of likelihood ratio, which is a well-known measure in the speaker recognition field, is used as a calibration performance metric. Evaluated on the challenging MOBIO and SCface databases, the results indicate that through linear calibration the scores produced by the face recognition system can be less misleading in its likelihood ratio interpretation. In addition, it is shown through the categorical calibration experiments that calibration can be used not only to assure likelihood ratio interpretation of scores, but also improving the verification performance of face recognition system.},
            pdf = {https://publications.idiap.ch/attachments/papers/2014/I.Mantasari_IETBMT_2014.pdf}
}



crossreferenced publications: 
@TECHREPORT{I.Mantasari_Idiap-RR-01-2014,
         author = {I. Mantasari, Miranti and G{\"{u}}nther, Manuel and Wallace, Roy and Saedi, Rahim and Marcel, S{\'{e}}bastien and Van Leeuwen, David},
       keywords = {calibration, forensic face recognition, likelihood ratio, linear score transformation.},
       projects = {BBfor2},
          month = {1},
          title = {Score Calibration in Face Recognition},
           type = {Idiap-RR},
         number = {Idiap-RR-01-2014},
           year = {2014},
    institution = {Idiap},
       abstract = {This paper presents an evaluation of verification and calibration performance of a face recognition system based on inter-session variability modeling. As an extension to the calibration through
linear transformation of scores, categorical calibration is introduced as a way to include additional
information of images to calibration. The cost of likelihood ratio, which is a well-known measure in
the speaker recognition field, is used as a calibration performance metric. Evaluated on the challenging MOBIO and SCface databases, the results indicate that through linear calibration the scores
produced by the face recognition system can be less misleading in its likelihood ratio interpretation. In addition, it is shown through the categorical calibration experiments that calibration can be
used not only to assure likelihood ratio interpretation of scores, but also improving the verification
performance of face recognition system.},
            pdf = {https://publications.idiap.ch/attachments/reports/2013/I.Mantasari_Idiap-RR-01-2014.pdf}
}