Abstract
We propose to use nonparametric functional data analysis techniques within the framework of a signature recognition system. Regarding the signature as a random function from \( \mathbb{R} {\rm(time \,domain)\,to}\, \mathbb{R}^2\) (position (x,y) of the pen), we tackle the problem as a genuine nonparametric functional classification problem, in contrast to currently used biometrical approaches. A simulation study on a real data set shows good results.
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© 2011 Springer-Verlag Berlin Heidelberg
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Geenens, G. (2011). A Nonparametric Functional Method for Signature Recognition. In: Ferraty, F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2736-1_22
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DOI: https://doi.org/10.1007/978-3-7908-2736-1_22
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Online ISBN: 978-3-7908-2736-1
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