Michael G. Lorenz

Handwriting signature is the most diffuse mean for personal identification. Lots of works have been carried out to get reasonable errors rates within automatic signature verification on-line. Most of the algorithms that have been used for matching work by features extraction. This paper deals with the analysis of discriminative powers of the features that can be extracted from an on-line signature, how it's possible to increase those discriminative powers by dynamic time warping as a step in the preprocessing of the signal coming from the tablet. Also it will be covered the influence of this new step in the performance of the Gaussian mixture models algorithm, which has been shown as a successfully algorithm for on-line automatic signature verification in recent studies. A complete experimental evaluation of the algorithm base on dynamic time warping and Gaussian Mixture Models has been conducted on 2500 genuine signatures samples and 2500 skilled forgery samples from 100 users. Those samples are included at the public access MCyT-Signature-Corpus Database.

Security is one of the major issues in IT systems. When users need to be authenticated, Biometrics appears as an improved alternative to the traditional systems. If biometrics is to be used in an IT product devoted to security, there should be some kind of assurance that no security holes are added. Even more, user biometric data is a very sensible piece of data. Therefore security achieved shall be evaluated, and a methodology is needed to cover such evaluation in an objective way. Based on previous works in security evaluation, such as Common Criteria and ISO, and some few initiatives in providing such concepts to the biometrics world, authors present in this paper an overview of such works, as well as some proposals to improve security evaluation in biometrics. This leads not only to such proposals, but also to some future works to be carried on in order to complete the work here shown