Abstract
To overcome difficulties in transferring “classical” handwriting examination methods into computer algorithms a hybrid neuronal system, proposed by Kosko [1], was employed to derive rules for signature region matching. The segmentation of signatures, written on paper documents, into regions will be presented and the two stage fuzzy rule learning, for finding and tuning the fuzzy rules will be discussed. By using the neuro-fuzzy approach [1] a region matching performance of 98% was achieved.
Due to the planned application there is the restriction of using just one reference signature per writer.
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© 2002 Springer-Verlag Berlin Heidelberg
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Franke, K., Zhang, YN., Köppen, M. (2002). Static Signature Verification Employing a Kosko-Neuro-fuzzy Approach. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_26
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DOI: https://doi.org/10.1007/3-540-45631-7_26
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Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-45631-5
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