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Signature of a person is one of the most popular and legally accepted behavioral biometrics that provides secure means for verification and personal identification in many applications such as financial, commercial and legal transactions. The objective of the signature verification system is to classify between genuine and forgery that is often associated with intrapersonal and interpersonal variability. Unlike other languages, Arabic has unique features; it contains diacritics, ligatures, and overlapping.  Because of lacking any form of dynamic information during the Arabic signature writing process, it will be more difficult to obtain higher verification accuracy. This paper addresses the above difficulty by introducing a novel Off-Line Arabic signature verification algorithm. Different from state-of-the-art works that adopt one-level of verification or multiple classifiers based on statistical learning theory; this work employs two-level of fuzzy set related verification. The level one verification depends on finding the total difference between the features extracted from the test signature and the mean values of each corresponding features in the training signatures (owning the same signature). Whereas, the level two verification relies on the output of the fuzzy logic module depending on the membership functions that has been created from the signature features in the training dataset for a specific signer. It is concluded from the experimental results that the verification system performs well and has the ability to reduce both False Acceptance Rate (FAR) and False Rejection Rate (FRR).

Distance and Fuzzy Classifiers Alliance: The Solution to Off-line Arabic Signature Verification System for Forensic Science