Handwritten Signature Verification Using Image Invariants and Dynamic Features

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 perceptron (MLP) with one input layer, one hidden layer and one output layer. The performance of the proposed system is presented through simulation examples