Abstract
Signatures are one of the most important behavioral biometric feature which are used to recognize an individual identity. These handwritten signatures are captured as actual input signals that are written on some electronic gadgets by the user. The divergent writing patterns of individuals primarily due to variation in style, shape and steadiness create real time challenges in differentiating real signatures from the fake ones. In order to overcome the said challenge of signature recognition, this article introduces model driven approach for dynamic signature verification. Particularly, a UMLPDSV (Unified Modeling Language Profile for Dynamic Signature Verification) has been proposed to specify the signature verification requirements at high abstraction level. This provides the basis to automatically generate target models of different machine learning tools (e.g. RapidMiner process, Matlab code etc.) to perform dynamic signature verification. The applicability of UMLPDSV has been validated through internet banking case study.
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Samea, F., Anwar, M.W., Azam, F., Khan, M., Shinwari, M.F. (2018). An Introduction to UMLPDSV for Real-Time Dynamic Signature Verification. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-319-99972-2_32
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DOI: https://doi.org/10.1007/978-3-319-99972-2_32
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