Combining Fisher Discriminant Analysis and probabilistic neural network for effective on-line signature recognition

The advent of new technologies enables capturing the dynamic of a signature. This has opened a new perspective for the possible use of signatures as a basis for an authentication system that is accurate and trustworthy enough to be integrated in practical applications. Automatic online signature recognition and verification is one of the biometric techniques being the subject of a growing and intensive research activity. In this paper, we address this problem and we propose a two-stage approach for personal identification. The first stage consists in the use of linear discriminant analysis to reduce the dimensionality of the feature space while maintaining discrimination between user classes. The second stage consists in tailoring a probabilistic neural network for effective classification purposes. Several experiments have been conducted using SVC2004 database. Very high classification rates have been achieved showing the effectiveness of the proposed approach.