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.