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.

This work has been supported by FEDER and MEC, TEC2016-77791-C4-2-R.

Authors and Affiliations

1. ESUP Tecnocampus (UPF), Av. Ermest Lluch 32, 08302, Mataró, Spain

   Carlos Alonso-Martinez & Marcos Faundez-Zanuy

Corresponding author

Correspondence toMarcos Faundez-Zanuy.

Editors and Affiliations

1. Department of Psychology, University of Campania Luigi Vanvitelli, Caserta, Italy

   Anna Esposito

2. Tecnocampus, Mataró, Spain

   Marcos Faundez-Zanuy

3. Department of Civil, Environment, Energy and Materials Engineering, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy

   Francesco Carlo Morabito

4. Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Turin, Italy

   Eros Pasero

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