Abstract
An Efficient signature recognition system is proposed in this paper. A new approach of application of krawtchouk moment method is used to estimate a set of feature vector which is an informative subset and is used as a input data for MFMCN classifier. Preprocessing step uses Krawtchouk moment, which automatically identifies the useful and common features consistently existing within different signature images of the same person. They are also invariant to shift, rotation, and scale. This moment method reduces the dimensionality of the input signature pattern by eliminating features containing low information or high redundancy. These features are used to recognize the signature using Modified Fuzzy Min-Max Neural Network with Compensatory Neuron (MFMCN). The proposed approach is applied to online signature recognition and experimentally it shows that it produces the excellent result by reducing the computational burden of the recognition system and provides high recognition rates.
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Chaudhari, B.M., Patil, R.S., Rane, K.P., Shinde, U.B. (2010). Online Signature Classification Using Modified Fuzzy Min-Max Neural Network with Compensatory Neuron Topology. In: Ranka, S., et al. Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14834-7_44
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DOI: https://doi.org/10.1007/978-3-642-14834-7_44
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