Learning features for offline handwritten signature verification using deep convolutional neural networks

Off-line signature verification based on grey level information using texture features

Several papers have recently appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP8,1riu2 plus LBP16,2riu2 and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white “nondistorting” background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.

Robustness of Offline Signature Verification Based on Gray Level Features

Deep Multitask Metric Learning for Offline Signature Verification

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.

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Offline handwritten signature verification — Literature review

An off-line signature verification system based on contour features is presented. It works at the local image level, and encodes directional properties of signature contours and the length of regions enclosed inside letters. Results obtained on a sub-corpus of the MCYT signature database shows that directional-based features work much better than length-based features. Results are comparable to existing approaches based on different features. It is also observed that combination of the proposed features does not provide improvements in performance, maybe to some existing correlation among them.

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S. Pecharroman

Papers

Off-line Signature Verification Using Contour Features