One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

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 one-class 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.