A novel two-stage protection scheme for automatic iris recognition systems against masquerade attacks carried out with synthetically reconstructed iris images is presented. The method uses different characteristics of real iris images to differentiate them from the synthetic ones, thereby addressing important security flaws detected in state-of-the-art commercial systems. Experiments are carried out on the publicly available Biosecure Database and demonstrate the efficacy of the proposed security enhancing approach.
A new method to generate synthetic online signatures is presented. The algorithm uses a parametrical model to
generate the synthetic Discrete Fourier Transform (DFT) of the trajectory signals, which are then refined in the
time domain and completed with a synthetic pressure function. Multiple samples of each signature are created
so that synthetic databases may be produced. Quantitative and qualitative results are reported, showing that,
in addition to presenting a very realistic appearance, the synthetically generated signatures have very similar
characteristics to those that enable the recognition of real signatures.
An automatic classification scheme of on-line handwritten signatures is presented. A Multilayer Perceptron
(MLP) with a hidden layer is used as classifier, and two different signature classes are considered, namely:
legible and non-legible name. Signatures are represented considering different feature subsets obtained from
global information. Mahalanobis distance is used to rank the parameters and feature selection is then applied
based on the top ranked features. Experimental results are given on the MCYT signature database comprising
330 signers. It is shown experimentally that automatic on-line signature classification based on the name legibility
is feasible.
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