Towards the design of an offline signature verifier based on a small number of genuine samples for training

Bouamra, Walid; Djeddi, Chawki; Nini, Brahim; Diaz, Moises; Siddiqi, Imran

doi:10.1016/j.eswa.2018.04.035

Cited by 43 publications

(18 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A first aspect that should be considered when working with HSV is the decision among which classification strategy to use, that is, WD vs. WI (Bouamra et al, 2018). If a verification model is trained for each writer, the system is called writer-dependent (WD).…”

Section: Introductionmentioning

confidence: 99%

A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification

Souza

Oliveira

Cruz

et al. 2020

Expert Systems with Applications

View full text Add to dashboard Cite

High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and difficulties of the offline Handwritten Signature Verification (HSV) problem. A good alternative to tackle these issues is to use a writerindependent (WI) framework. In WI systems, a single model is trained to perform signature verification for all writers from a dissimilarity space generated by the dichotomy transformation. Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context. In this work, we present a white-box analysis of this approach highlighting how it handles the challenges, the dynamic selection of references through fusion function, and its application for transfer learning. All the analyses are carried out at the instance level using the instance hardness (IH) measure. The experimental results show that, using the IH analysis, we were able to characterize "good" and "bad" quality skilled forgeries as well as the frontier region between positive and negative samples. This enables futures investigations on methods for improving discrimination between genuine signatures and skilled forgeries by considering * Corresponding author

show abstract

Section: Introductionmentioning

confidence: 99%

A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification

Souza

Oliveira

Cruz

et al. 2020

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…[6] authors have stated that, sometimes handcrafted features do not have much significance in the signature images, and suggested to use a good model for feature extraction. Recently many deep learning‐based architectures for signature verification have been proposed [7,9,11,14].…”

Section: Related Workmentioning

confidence: 99%

Multi‐task learning using GNet features and SVM classifier for signature identification

Jain

Singh

2021

IET Biometrics

View full text Add to dashboard Cite

Signature biometrics is a widely accepted and used modality to verify the identity of an individual in many legal and financial organisations. A writer and language‐independent signature identification method that can distinguish between the genuine and forged sample irrespective of the language of the signature has been proposed. To extract the distinguishing features, a pre‐trained model GoogLeNet, which is fine‐tuned with the largest signature dataset present till date (GPDS Synthetic), has been used. The proposed method is tested over the BHSig260 (contains images from two regional languages, Bengali and Hindi) dataset. With the help of the above fine‐tuned model, knowledge is transferred to the publicly available datasets – BHSig260 and MCYT‐75. The features extracted using the fine‐tuned model has been fed to the support vector machine (SVM) classifiers. With the proposed method, 96.5% and 95.7% accuracy on Bengali and Hindi datasets, and 93% on MCYT‐75 with skilled forged samples have been achieved respectively.

show abstract

“…Keypoints (SIFT [45], ** SURF [163], ** BRISK [61], ** KAZE [184], ** FREAK [159]**), run length [30], ** texture [255], ** quad-tree [232], ** grid based [278], ** shadow code [66,223], ** ...…”

Section: -Pixel Orientedmentioning

confidence: 99%

A Perspective Analysis of Handwritten Signature Technology

Diaz¹,

et al. 2019

Self Cite

View full text Add to dashboard Cite

Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject. CCS Concepts: • General and reference → Surveys and overviews; • Security and privacy → Biometrics; Access control; • Theory of computation → Pattern matching; • Applied computing → Document analysis; System forensics;

show abstract

Towards the design of an offline signature verifier based on a small number of genuine samples for training

Cited by 43 publications

References 38 publications

A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification

A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification

Multi‐task learning using GNet features and SVM classifier for signature identification

A Perspective Analysis of Handwritten Signature Technology

Contact Info

Product

Resources

About