Dynamic signature recognition based on velocity changes of some features
International Journal of Biometrics
Off-line signature recognition using morphological pixel variance analysis
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Signature recognition using vector quantization
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Determining the similarity of signatures on the basis of characteristic points analysis
International Journal of Biometrics
Learning Vector Quantisation based recognition of offline handwritten signatures
International Journal of Biometrics
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In this paper, a development of automatic signature classification system is proposed. We have presented offline and online signature verification system, based on the signature invariants and its dynamic features. The proposed system segments each signature based on its perceptually important points and then, for each segment, computes a number of features that are scale, rotation and displacement invariant. The normalized moments and the normalized Fourier descriptors are used for this invariancy, while the speed of pen is used as a dynamic feature of the signature. In both cases the data acquisition, pre-processing, feature extraction and comparison steps are analyzed and discussed. Both static and dynamic features were used as an input to a neural network. The neural network used for classification is a multi-layer perception (MLP) with one input layer, one hidden layer and one output layer. The performance of the proposed system is presented through simulation examples.