Online Handwriting and Signature Normalization and Fusion in a Biometric Security Application

Abstract

In this paper, we analyze the combined application of signatures and capital handwriting in a biometric recognition application. We combine a signature recognition system based in a multi-section vector quantization with a handwriting text recognition system based in self-organizing maps and DTW. Due to the need to normalize the scores before the combination, we study the effect of different normalization methods and we propose the application of a logarithmic transformation for signature scores previous normalize them. Experimental results show that the identification rate raises from 86.11% using capital letter words and 96.95% using signatures up to 99.72% with a fusion of both traits. Minimum detection cost function (DCF) also improves, from 3.56 and 3.51%, respectively, up to 1.0% using the fusion of both traits.