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.
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Acknowledgements
This work has been supported by FEDER and MEC, TEC2016-77791-C4-2-R.
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Alonso-Martinez, C., Faundez-Zanuy, M. (2020). Online Handwriting and Signature Normalization and Fusion in a Biometric Security Application. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_40
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DOI: https://doi.org/10.1007/978-981-13-8950-4_40
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