Forensic Document Examiners’ Skill in Distinguishing Between Natural and Disguised Handwriting Behaviors

Bird, Carolyne; Found, Bryan; Rogers, Doug

doi:10.1111/j.1556-4029.2010.01456.x

Cited by 27 publications

(14 citation statements)

References 12 publications

(21 reference statements)

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“…There has been a research effort towards descriptor development and feature selection in dynamic signatures [9,10,[147][148][149][150] or adapting automated comparison systems to forensic purposes [151][152][153][154]. Furthermore, the criticism has led forensic scientists to provide evidence for expert opinion reliability [155][156][157][158][159][160], to reconsider the identification and unicity paradigms [161,162] and to publish their methods [76] and best practices [163,164].…”

Section: Methodology In Dynamic Signature Examinationmentioning

confidence: 99%

Dynamic signatures: A review of dynamic feature variation and forensic methodology

Linden

Marquis

Bozza

et al. 2018

Forensic Science International

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This article focuses on dynamic signatures and their features. It provides a detailed and critical review of dynamic feature variations and circumstantial parameters affecting dynamic signatures. The state of the art summarizes available knowledge, meant to assist the forensic practitioner in cases presenting extraordinary writing conditions. The studied parameters include hardware-related issues, aging and the influence of time, as well as physical and mental states of the writer. Some parameters, such as drug and alcohol abuse or medication, have very strong effects on handwriting and signature dynamics. Other conditions such as the writer's posture and fatigue have been found to affect feature variation less severely. The need for further research about the influence of these parameters, as well as handwriting dynamics in general is highlighted. These factors are relevant to the examiner in the assessment of the probative value of the reported features. Additionally, methodology for forensic examination of dynamic signatures is discussed. Available methodology and procedures are reviewed, while pointing out major technical and methodological advances in the field of forensic handwriting examination. The need for sharing the best practice manuals, standard operating procedures and methodologies to favor further progress is accentuated.

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Section: Methodology In Dynamic Signature Examinationmentioning

confidence: 99%

Dynamic signatures: A review of dynamic feature variation and forensic methodology

Linden

Marquis

Bozza

et al. 2018

Forensic Science International

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“…They depend on handwritten text features as slant, commencement and terminating strokes, size or style of words, spacing, pressure, frequency of words, letter proportions, and many others [8]. It is also important to note that in the field of forensics, what separates an untrained person from a skilled investigator is the rate of false positives and the number of cases for which only an inconclusive opinion about the authors' identity may be expressed [9]. Many systems help to analyze a handwritten text [10][11][12][13][14], which utilizes such experts' features.…”

Section: Introductionmentioning

confidence: 99%

An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks

et al. 2021

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Handwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated analysis method for the dynamic electronic representation of handwritten signature authentication was researched. The developed algorithms are based on the dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the designed electronic pen described in the paper. The triplet loss method was used to train a neural network suitable for writer-invariant signature verification. For each signature, the same neural network calculates a fixed-length latent space representation. The hand-corrected dataset containing 10,622 signatures was used in order to train and evaluate the proposed neural network. After learning, the network was tested and evaluated based on a comparison with the results found in the literature. The use of the triplet loss algorithm to teach the neural network to generate embeddings has proven to give good results in aggregating similar signatures and separating them from signatures representing different people.

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“…Ambas as conclusões conduzem ao erro quanto à opinião de autoria. Ainda, alguns peritos podem não encontrar diferenças entre a assinatura questionada e as assinaturas padrão, ou ainda presumir que as diferenças foram produto de variação natural do autor [10]. Tabela 1.…”

Section: Introductionunclassified

Estudo das características gráficas mais frequentemente alteradas em disfarces de assinaturas

Gorziza

2017

Rev. Bras. Crimin.

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ResumoAssinaturas são os grafismos mais efetuados pelas pessoas, por demonstrar conhecimento e concordância a documentos diversos. O exame grafoscópico de assinaturas disfarçadas é uma das determinações mais difíceis de proceder na Documentoscopia. Os disfarces podem confundir os peritos e conduzir a erros quanto à autoria de assinaturas. O objetivo deste trabalho foi identificar as características mais frequentes encontradas em análises grafoscópicas de assinaturas disfarçadas. Foi solicitado a 102 voluntários que assinassem sua assinatura habitual por cinco vezes e, então, que tentassem disfarçar sua assinatura. Posteriormente, realizou-se o confronto gráfico entre as assinaturas padrão e as disfarçadas, e as alterações encontradas foram anotadas, sendo que as mais frequentes foram: modificações da forma de letras maiúsculas e minúsculas, de dimensão e calibre, na forma e na posição de ataques e remates, na quantidade de levantamentos da caneta durante a escrita (andamento gráfico) e a alteração ou supressão de símbolos ou caracteres especiais. Esses dados podem ajudar os peritos a identificar características gráficas indicativas de disfarces, quando da verificação de autenticidade. Por outro lado, observou-se que características alteradas com menor frequência surgem em assinaturas disfarçadas e podem levar a uma conclusão errônea no sentido da falsificação do lançamento gráfico.Palavras-Chave: Grafoscopia; Perícia Grafoscópica; Disfarce de Assinaturas. AbstractSignatures are the most common graphisms produced by people, which demonstrate acknowledgement and agreement to several documents. Signature disguise exam is among the most complicated kind of forged documents analysis. Disguises can confound the experts and lead to errors as to the authorship of a signature. This study aimed to identify the most frequent features found in the analysis of signature disguise. 102 volunteers were asked to write their signatures 5 times and then try to disguise it. Then, a confrontation between their original signatures and the disguised ones was carried out, in which all the observed characteristics were recorded. The most frequent ones were: alteration in the format of upper and lowercase letters, general size, change in initial and terminal strokes, variations in the quantity of pen lifts during the writing and alteration or suppression of a symbol. This body of data might help experts to suspect of disguises, but even others unusual characteristics can also appear in disguises and may lead to misinterpret them as forgery.

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Forensic Document Examiners’ Skill in Distinguishing Between Natural and Disguised Handwriting Behaviors

Cited by 27 publications

References 12 publications

Dynamic signatures: A review of dynamic feature variation and forensic methodology

Dynamic signatures: A review of dynamic feature variation and forensic methodology

An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks

Estudo das características gráficas mais frequentemente alteradas em disfarces de assinaturas

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