Abstract
It has been observed that every signature is distinctive, and that’s why, the use of signatures as a biometric has been supported and implemented in various technologies. It is almost impossible for a person himself to repeat the same signature every time he signs. We proposed an intelligent system for off-line signature verification using chain-code. Dynamic features are not available, so, it becomes more difficult to achieve the goal. Chain-code is extracted locally and Feed Forward Back Propagation Neural Network used as a classifier. Chain-code is a simple directional feature, extracted from a thinned image of signature because contour based system acquires more memory. An intelligent network is proposed for training and classification. The results are compared with a very basic energy density method. Chain-code method is found very effective if number of samples available for training is limited, which is also practically feasible.
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Tomar, M., Singh, P. (2011). An Intelligent Network for Offline Signature Verification Using Chain Code. In: Meghanathan, N., Kaushik, B.K., Nagamalai, D. (eds) Advanced Computing. CCSIT 2011. Communications in Computer and Information Science, vol 133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17881-8_2
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DOI: https://doi.org/10.1007/978-3-642-17881-8_2
Publisher Name: Springer, Berlin, Heidelberg
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