Automatic Measures for Predicting Performance in Off-Line Signature

Performance in terms of accuracy is one of the most important goals of a biometric system. Hence, having a measure which is able to predict the performance with respect to a particular sample of interest is specially useful, and can be exploited in a number of ways. In this paper, the authors present two automatic measures for predicting the performance in off-line signature verification. Results obtained on a sub-corpus of the MCYT signature database confirms a relationship between the proposed measures and system error rates measured in terms of Equal Error Rate (EER), False Acceptance Rate (FAR) and False Rejection Rate (FRR).