Score Calibration in Face Recognition

This paper presents an evaluation of the verification and calibration performance of a face recog-nition system based on inter-session variability modeling. As an extension to calibration through linear transformation of scores, categorical calibration is introduced as a way to include additional information about images for calibration. The cost of likelihood ratio, which is a well-known mea-sure in the speaker recognition field, is used as a calibration performance metric. Results on the challenging MOBIO and SCface databases indicate that linearly calibrated face recognition scores are less misleading in their likelihood ratio interpretation than uncalibrated scores. In addition, the categorical calibration experiments show that calibration can be used not only to improve the likelihood ratio interpretation of scores, but also to improve the verification performance of a face recognition system