Offline handwritten signature verification — Literature review

@article{Hafemann2017OfflineHS,
  title={Offline handwritten signature verification — Literature review},
  author={Luiz G. Hafemann and Robert Sabourin and Luiz Oliveira},
  journal={2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)},
  year={2017},
  pages={1-8}
}
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… 
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References

SHOWING 1-10 OF 107 REFERENCES
Analyzing features learned for Offline Signature Verification using Deep CNNs
TLDR
The analysis shows that the model is very effective in separating signatures that have a different global appearance, while being particularly vulnerable to forgeries that very closely resemble genuine signatures, even if their line quality is bad, which is the case of slowly-traced forgeries.
Writer-independent feature learning for Offline Signature Verification using Deep Convolutional Neural Networks
TLDR
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.
Signature Embedding: Writer Independent Offline Signature Verification with Deep Metric Learning
TLDR
A new approach to the writer independent verification of offline signatures is proposed, named Signature Embedding, which is based on deep metric learning and learns to embed signatures into a high-dimensional space, in which the Euclidean distance functions as a metric of their similarity.
Multi-feature approach for writer-independent offline signature verification
TLDR
A novel offline signature verification system based on multiple feature extraction techniques, dichotomy transformation and boosting feature selection that provides an efficient solution to a wide range problems with limited training samples, new training samples emerging during operations, numerous classes, and few or no counterexamples.
Multi-classifier systems for off-line signature verification
TLDR
The approaches proposed in this Thesis employ the concept of multi-classifier systems (MCS) based on HMMs to learn signatures at several levels of perception and the proposal of a hybrid generative-discriminative classification architecture.
Persian Signature Verification using Convolutional Neural Networks
TLDR
In this paper, an offline signature verification scheme based on Convolutional Neural Network (CNN) is proposed and the simulation results reveal the efficiency of the suggested algorithm.
ICDAR 2013 Competitions on Signature Verification and Writer Identification for On- and Offline Skilled Forgeries (SigWiComp 2013)
This paper presents the results of the ICDAR2013 competitions on signature verification and writer identification for on- and offline skilled forgeries jointly organized by PR researchers and
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