Abstract
In this paper we present a novel and efficient approach for off-line signature verification and identification using Support Vector Machine. The global, directional and grid features of the signatures were used. In verification, one-against-all strategy is used. The true acceptance rate is 98% and true rejection rate is 81%. As the identification of signatures represent a multi-class problem, Support Vector Machine’s one-against-all and one-against-one strategies were applied and their performance were compared. Our experiments indicate that one-against-one with 97% true recognition rate performs better than one-against-all by 3%.
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Özgündüz, E., Şentürk, T., Karslıgil, M.E. (2005). Efficient Off-Line Verification and Identification of Signatures by Multiclass Support Vector Machines. In: Gagalowicz, A., Philips, W. (eds) Computer Analysis of Images and Patterns. CAIP 2005. Lecture Notes in Computer Science, vol 3691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556121_98
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DOI: https://doi.org/10.1007/11556121_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28969-2
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