Online Signature Processing for Biometric User Authentication and Identification

Online signature recognition is a prominent biometric system reliable for users’ verification that leverages the dynamic aspects of signatures. This paper presents a novel approach for dynamic signature verification and identification by combining temporal and spectral analysis techniques. The proposed method extracts discriminative features from both spatial coordinates and their derivatives, such as velocity, acceleration, and pen speed. It also applies data augmentation techniques such as rotation, scaling, and temporal distortion, to generate static images of the signatures and use spectrograms for further analysis. The experiments carried out on the SVC2004 Task 2 database, which includes genuine and skilled forgery signatures, have shown the effectiveness of the proposed method. Indeed, for verification mode, the use of Artificial neural network allows achieving a lower EER of up 2.11%, highlighting its potential for reliable signature verification. Furthermore, exploring signature images with Convolutional neural network for identification mode, allows achieving an accuracy of 99.56%. This study highlights the effectiveness of combining temporal and spectral features for signature identification and verification in security-sensitive applications.