Spline Interpolation and Deep Neural Networks as Feature Extractors for Signature Verification Purpo

Digital security in modern systems very often uses biometric, and increasingly, new implementations appear. Such applications can be found everywhere, even when picking up the package from courier, we certify its receipt through our signature on the tablet. However, verification of this form is not one of the simplest elements in information processing systems. Given the different sizes, angles, or writing conditions that may affect its stability, new methods to evaluate signatures are constantly needed. In this article, we propose the use of spline interpolation and two types of artificial neural networks to verify the identity of a person based on selected local and global features extracted from the image of a signature. Global features are extracted concerning interpolation and graphic processing methods, while local features are verified using convolutional neural networks. Both sets of features are used in the identity verification process. The article presents the model of the operation together with experiments, taking into account various parameters of the proposed extraction. We have reached an accuracy of 87.7% on the SVC2004 database.