ARTICLE
I.Mantasari_IETBMT_2014/IDIAP
Score Calibration in Face Recognition
Mandasari, Miranti I.
Günther, Manuel
Wallace, Roy
Saedi, Rahim
Marcel, Sébastien
Van Leeuwen, David
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
EXTERNAL
https://publications.idiap.ch/attachments/papers/2014/I.Mantasari_IETBMT_2014.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/I.Mantasari_Idiap-RR-01-2014
Related documents
IET Biometrics
1-11
2047-4938
2014
http://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2013.0066
URL
10.1049/iet-bmt.2013.0066
doi
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.
REPORT
I.Mantasari_Idiap-RR-01-2014/IDIAP
Score Calibration in Face Recognition
I. Mantasari, Miranti
Günther, Manuel
Wallace, Roy
Saedi, Rahim
Marcel, Sébastien
Van Leeuwen, David
calibration
forensic face recognition
likelihood ratio
linear score transformation.
EXTERNAL
https://publications.idiap.ch/attachments/reports/2013/I.Mantasari_Idiap-RR-01-2014.pdf
PUBLIC
Idiap-RR-01-2014
2014
Idiap
January 2014
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.