Abstract
A signature verification algorithm based on static and dynamic features of online signature data is presented. Texture and topological features are the static features of a signature image whereas the digital tablet captures in real-time the pressure values, breakpoints, and the time taken to create a signature. 1D – log Gabor wavelet and Euler numbers are used to analyze the textural and topological features of the signature respectively. A multi-classifier decision algorithm combines the results obtained from three feature sets to attain an accuracy of 98.18%.
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© 2004 Springer-Verlag Berlin Heidelberg
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Vatsa, M., Singh, R., Mitra, P., Noore, A. (2004). Signature Verification Using Static and Dynamic Features. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_53
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DOI: https://doi.org/10.1007/978-3-540-30499-9_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
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