Writer-Independent Offline Signature Verification Using Local and Global Feature Fusion

In offline signature verification, extracting effective features and enhancing identification accuracy remain critical challenges. Traditional feature extraction modules struggle to capture comprehensive, detailed characteristics from signatures alone and often suffer from overfitting issues. This paper introduces ResT, a novel approach that integrates the Residual Network (ResNet) and Transformer architectures for multi-scale feature extraction. The proposed method comprises two components: ResNet blocks and Transformer blocks, designed to extract local and global signature characteristics, respectively. Within the ResNet blocks, we integrate two modules—Spatial-to-Depth Convolution (SPD-Conv) and Spatial and Channel Reconstruction Convolution (SCConv)—to emphasize stroke-level features, thereby improving local feature extraction. For the Transformer blocks, Vision Transformer (ViT) is employed to analyze the signature’s overall shape and stroke trends, capturing global features. To evaluate performance, we implement a writer-independent (WI) verification system and conduct extensive experiments on four public datasets: GPDS, CEDAR, BHSig-Bengali, and BHSig-Hindi. Results demonstrate that the proposed ResT model effectively distinguishes genuine from forged signatures and achieves competitive performance compared to existing methods.