Local Gradient Hexa Pattern: A Descriptor for Signature Verification

Signature verification plays a crucial role in biometric authentication systems. Traditional local texture descriptors such as Local Binary Pattern (LBP), Local Directional Pattern (LDP), and Local Tetra Pattern (LTrP) exhibit limitations in capturing detailed gradient variations in signature strokes. This paper proposes a novel Local Gradient Hexa Pattern (LGHP) descriptor for robust feature extraction in offline signature verification. The proposed method encodes gradient magnitude and directional information into a six-pattern structure, enhancing discrimination capability. Experimental evaluation is performed on standard signature datasets using a Minimum Distance Classifier. Performance comparison demonstrates that the proposed LGHP achieves improved verification accuracy and reduced false acceptance rate compared to existing descriptors. The results confirm the effectiveness and robustness of the proposed approach for biometric authentication systems.