What is the error margin of your signature analysis?

Marquis, Raymond; Cadola, Liv; Mazzella, Williams; Hicks, Tacha

doi:10.1016/j.forsciint.2017.11.012

Cited by 9 publications

(12 citation statements)

References 32 publications

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“…We agree with the response in [1] that the appropriate way for a forensic practitioner to express their conclusions is as a numeric likelihood ratio value (which cannot be expressed as a percentage). We disagree, however, with the recommendations in [1] as to how a forensic practitioner should generate the likelihood ratio value that they report.…”

Section: Accepted Manuscriptsupporting

confidence: 71%

“…If, even under the best case scenario there remains an unavoidable error margin for this analysis according to the state of scientific and technical knowledge, please state what that error margin is (preferably in %).' ( [1] p. e1)…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…A case report [1] recently published in Forensic Science International ostensibly presents a model of how to respond to a request from a court to:…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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A response to Marquis et al. (2017) What is the error margin of your signature analysis?

Morrison

Ballentyne

Geoghegan

2018

Forensic Science International

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Section: Accepted Manuscriptsupporting

confidence: 71%

Section: Accepted Manuscriptmentioning

confidence: 99%

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A response to Marquis et al. (2017) What is the error margin of your signature analysis?

Morrison

Ballentyne

Geoghegan

2018

Forensic Science International

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“…Marquis et al describe a static signature case, approached using the likelihood ratio approach and personal probabilities [207]. Gonzalez-Rodriguez et al developed a way to use Likelihood Ratios in…”

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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“…The observed characteristics are then systematically compared between the questioned and reference material in the comparison stage. In the evaluation stage, the signification of similarities and differences are evaluated to infer about a possible common origin between the questioned and reference material [30]. When possible, verification is carried out by a second expert.…”

Section: Activity 3: Preparation Of a Mock Casementioning

confidence: 99%

The potential of collaborative learning as a tool for forensic students: Application to signature examination

et al. 2020

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Transferring theoretical knowledge to practical skills remains a big challenge in forensic science, especially in questioned documents. The examination of handwriting and signatures requires years of practice to develop the necessary skills. While students (and to some extent the general population) often have the impression that it is easy to differentiate handwriting from different persons, in practice, particularly when dealing with simulated signatures, there is a high risk of reaching a wrong conclusion when questioned document experts do not use a systematic approach and/or are not sufficiently experienced (see for example the famous French Dreyfus case). Thus, a novel teaching approach, based on collaborative learning, has been introduced in a theoretical handwriting class to improve the students' theoretical knowledge, and additionally make them aware of the limitations of their practical skills and give them tools to improve them in their future practice. Through five activities, the students took the roles of victims, forgers, teachers and experts and created their own learning materials (i.e. signatures and mock casework). During those interactive activities, they learned to describe their signature's characteristics, intra-variability and complexity, and thus evaluate their own signature's vulnerability (as potential victims). They learned techniques to simulate signatures and detect the resulting forgeries' characteristics (in the role of forgers). In the role of teachers, they prepared mock casework scenarios and gave feedback to their colleague's examination of the produced material. As experts, they carried out signature examination as they would in a proficiency test and were exposed to the difficulties an actual expert may encounter in practice. The evaluation of this novel teaching scenario was very positive, as students learned more extensively the possibilities and limitations of signature comparison. They were more active and motivated in their learning experiences. The teaching team also had an improved experience. Some students complained of an increased workload and imprecise instructions. Improvements were tested and are discussed in this paper.

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What is the error margin of your signature analysis?

Cited by 9 publications

References 32 publications

A response to Marquis et al. (2017) What is the error margin of your signature analysis?

A response to Marquis et al. (2017) What is the error margin of your signature analysis?

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

The potential of collaborative learning as a tool for forensic students: Application to signature examination

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