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MultiScale Fusion of Histogram-based Features for Robust Off-line Handwritten Signature Verification

Developing robust signature verification systems is one of the most attracting topics in the handwriting recognition field. In fact, dealing with signature spoofing requires the use of robust features that help to distinguish authentic signatures from the forged ones. Presently, we propose a multiscale fusion of two histogram-based features to perform signatures description. Precisely, we introduce the Local Difference Feature as new descriptor that is fused with the Histogram Of Templates. These features are calculated on a multiscale neighborhood to highlight pixels distribution within the signature shape. The verification stage is achieved by using SVM classifier. Performance assessment is carried out on GPDS-300 and MCYT-75 datasets. Results in terms of average error rates evince the robustness of the proposed features, which outperform various state of the art methods.