Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection. It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages, including noninvasiveness, user-friendliness, and social and legal acceptability. According to the literature, extensive research has been conducted on signature verification systems in a variety of languages, including English, Hindi, Bangla, and Chinese. However, the Arabic Offline Signature Verification (OSV) system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes, diacritics, ligatures, and overlapping, making verification more difficult. Recently, signature verification systems have shown promising results for recognizing signatures that are genuine or forgeries; however, performance on skilled forgery detection is still unsatisfactory. Most existing methods require many learning samples to improve verification accuracy, which is a major drawback because the number of available signature samples is often limited in the practical application of signature verification systems. This study addresses these issues by presenting an OSV system based on multifeature fusion and discriminant feature selection using a genetic algorithm (GA). In contrast to existing methods, which use multiclass learning approaches, this study uses a oneclass learning strategy to address imbalanced signature data in the practical application of a signature verification system. The proposed approach is tested on three signature databases (SID-Arabic handwriting signatures, CEDAR (Center of Excellence for Document Analysis and Recognition), and UTSIG (University of Tehran Persian Signature), and experimental results show that the proposed system outperforms existing systems in terms of reducing the False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (ERR). The proposed system achieved 5% improvement.

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Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER).