A novel biometric system for signature verification based on score level fusion approach

The active modality of handwriting is broadly related to signature verification in the context of biometric user authentication systems. Signature verification aims to verify a questioned signature as being genuine or forged compared to some previously provided signatures from the claimed person. By doing so, we may be able to verify a person's identity at accuracy and speed even better than human performance. Application areas of signature verification include different purposes and principally in access controls and forensic document examination. This work presents a novel biometric system for signature verification. We propose a new model that we called the Extended Beta-elliptic model and we integrate the fuzzy elementary perceptual codes (FEPC) to extract static and dynamic features. To discriminate the genuine and forgery signatures of a user, we explore a fusion using the sum rule combiner of three scores which are deep bidirectional long short-term memory (deep BiLSTM), support vector machine (SVM) with Dynamic Time Warping (DTW), and SVM with a new proposed parameter comparator. Our system has been evaluated on two publicly available online signature databases namely SVC2004 Task 2 and MCYT-100, and it shows promising performance gains.