A Binarized Multi-Resolution Feature-Based Offline Signature

When a higher level of exactness is needed, handwritten signature recognition is mainly employed to certify administrative and official papers. Despite extensive previous research, offline signature recognition often remains a challenge, especially when distinguishing authentic signatures from forgeries. Indeed, the difference in appearance between a true and a forged signature may be much smaller than that of authentic signatures. Hence, the present paper outlines a new approach for the offline signature representation using multi-scale analysis. Indeed, this last presentation is intended to capture texture features over a wide range of resolutions. It is designed from binarized statistical features of the image, which are computed at different scales. The pre-learned filters, derived from natural images, are implemented on the signature images to reveal the signature structure and generate a discriminative image description. The reduced relevant data is assessed using the classifier for efficient offline signature recognition.