Offline handwritten signature verification — Literature review
Luiz G. Hafemann,Robert Sabourin,Luiz S. Oliveira +2 more
- pp 1-8
TLDR
How the problem has been handled in the past few decades is presented, the recent advancements in the field are analyzed, and the potential directions for future research are analyzed.Abstract:
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.read more
Citations
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Journal ArticleDOI
Learning features for offline handwritten signature verification using deep convolutional neural networks
TL;DR: A novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user is proposed.
Journal ArticleDOI
A Perspective Analysis of Handwritten Signature Technology
Moises Diaz,Miguel Ferrer,Donato Impedovo,Muhammad Imran Malik,Giuseppe Pirlo,Réjean Plamondon +5 more
TL;DR: A systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario is reported, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.
Journal ArticleDOI
Deep Multitask Metric Learning for Offline Signature Verification
TL;DR: Results of the experiments show that DMML achieves better performance compared to other methods in verifying genuine signatures, skilled and random forgeries.
Journal ArticleDOI
Image-based handwritten signature verification using hybrid methods of discrete Radon transform, principal component analysis and probabilistic neural network
TL;DR: The proposed framework aims to distinguish forgeries from genuine signatures based on the image level through hybrid methods of discrete Radon transform (DRT), principal component analysis (PCA) and probabilistic neural network (PNN).
Proceedings ArticleDOI
Writer-independent feature learning for Offline Signature Verification using Deep Convolutional Neural Networks
TL;DR: This work uses Deep Convolutional Neural Networks to learn features in a writer-independent format, and uses this model to obtain a feature representation on another set of users, where it is shown that the features learned in a subset of the users are discriminative for the other users.
References
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