Persian Signature Verification using Convolutional Neural Networks

The style of people’s handwritten signature is a biometric feature used in person authentication. In this paper, an offline signature verification scheme based on Convolutional Neural Network (CNN) is proposed. CNN focuses on the problems of feature extraction without prior knowledge on the data. The classification task is performed by Multilayer perceptron network (MLP). This method is not only capable of extracting features relevant to a given signature, but also robust with regard to signature location changes and scale variations when compared to classical methods. The proposed method is evaluated on a dataset of Persian signatures gathered originally from 22 people. The simulation results reveal the efficiency of the suggested algorithm.