A hybrid machine learning framework for offline signature verification using gray wolf optimization

Handwritten signature verification is one of the frequently used biometric in administrative, financial, legal, and similar scenarios to verify the identity of a person. Unfortunately, the offline nature of this task makes it more challenging as it involves intra-signature variance, and various temporal and environmental factors. This paper presents a novel offline signature verification system (OSVS) called SignGuard. It is designed by using Gray Wolf Optimization (GWO) for preprocessing. Also, Principal Orientation Alignment (POA) is used to mitigate their rotation as the proposed method has rotation sensitive descriptors. It is followed by two writer-independent models with new texture features namely, Centre Symmetric local binary pattern (CS-LBP) and Orthogonal Central Symmetric Local Binary Pattern (OC-CSLBP). They are trained using hybrid machine learning framework such that Support Vector Machine and XGBoost classifiers are integrated for the verification of a signature image in an offline mode. The performance of SignGuard is exploited on CEDAR, SID, and BHSig260 datasets, along with a novel dataset called DeepSignVault, such that OC-CSLBP and CS-LBP yields an accuracy of 98.77% and 97.46%, respectively. It is observed that SignGuard outperforms the existing OSVSs. The proposed architecture shows enhanced security and reliability for real-world applications in business, legal, and administrative systems. The outstanding performance of SignGuard highlights the authenticity of legal documents and financial transactions.