Abstract
The successful implementation of an automatic biometric system relies mainly on the consistency of the used training sets. Signatures of the same writer are similar but not identical, since they can differ both globally and locally, in location, scale, and orientation. In contrast with fingerprints, signatures that are completely authentic never exist. This paper emphasizes the application of a competitive neural network architecture for checking the consistency of the data set belonging to an individual in a biometric database. A neural network based consistency measure is proposed to quantify the intra-variability of the individuals signatures. A new democratic neural network architecture is then presented for minimization of the rejection error and maximization of the percentage of correct classification based on some well-known features and a new feature set.