A Hybrid Deep Visual and Structural Graph-Based Approach for Writer-Dependent Offline Signature Verification

This paper proposes a hybrid writer-dependent offline signature verification framework that integrates deep visual features with structural graph-based representations. A convolutional neural network inspired by SigNet is employed to extract global visual descriptors, while a graph neural network captures stroke-level structural characteristics from skeletonized signatures. The two feature sets are fused and refined using ReliefF-based feature selection. Writer-specific models are then constructed using centroid-based distance measures and adaptive thresholds estimated through Equal Error Rate (EER) optimization. Extensive experiments conducted on the CEDAR offline signature dataset using multiple train and test splits demonstrate that the proposed approach consistently achieves low error rates and high verification accuracy of 93.40%. The results confirm that combining visual and structural information significantly improves robustness against skilled forgeries.