Detection of Deception Via Handwriting Behaviors Using a Computerized Tool: Toward an Evaluation of Malingering

Luria, Gil; Kahana, Allon; Rosenblum, Sara

doi:10.1007/s12559-014-9288-6

Cited by 21 publications

(10 citation statements)

References 21 publications

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“…The lessons learned from the mentioned body of evidence can be profitably used for other health and psychological domains. For example, in Reference [115], handwriting was employed with successful results to the problem of recognizing malingering in health care, i.e., the false information given by patients about their health. Preliminary results suggested that a computerized tool based on handwriting can help detect deception.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

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Section: Discussionmentioning

confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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“…In [14], similar research is conducted, based on the same assumption that for people it is easier to tell the truth than to lie; hence, we need to see changes in both velocity and temporal spaces when analyzing the handwriting features. Conducted in 11 languages, this research demonstrates the same point as in [13], with the specific purpose of helping managers pinpoint sudden emotional changes and decode handwritten messages to reveal the true meaning of those messages as well as detect lies.…”

Section: Related Workmentioning

confidence: 70%

“…Another direction for many studies involving handwriting analysis is the detection of deceit. Luria et al [13] show such research where a non-intrusive system analyzes the handwriting in the context of healthcare with the purpose of detecting the false information that patients provide about their health. As current ways of determining deception are invasive and do not comply with a clinician-patient relationship, such an approach of using the handwriting as a tool is attractive from research perspectives.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Big Five personality traits from handwriting

Gavrilescu

Vizireanu

2018

J Image Video Proc.

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We propose the first non-invasive three-layer architecture in literature based on neural networks that aims to determine the Big Five personality traits of an individual by analyzing offline handwriting. We also present the first database in literature that links the Big Five personality type with the handwriting features collected from 128 subjects containing both predefined and random texts. Testing our novel architecture on this database, we show that the predefined texts add more value if enforced on writers in the training stage, offering accuracies of 84.4% in intra-subject tests and 80.5% in inter-subject tests when the random dataset is used for testing purposes, up to 7% higher than when random datasets are used in the training phase. We obtain the highest prediction accuracy for Openness to Experience, Extraversion, and Neuroticism (over 84%), while for Conscientiousness and Agreeableness, the prediction accuracy is around 77%. Overall, our approach offers the highest accuracy compared with other state-of-the-art methods and results are computed in maximum 90 s, making the approach faster than the questionnaire or psychological interviews currently used for determining the Big Five personality traits. Our research also shows there are relationships between specific handwriting features and prediction with high accuracy of specific personality traits and this can be further exploited for improving, even more, the prediction accuracy of the proposed architecture.

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“…To study personality disorder from psychological perspective psychologists concentrate on behavior aspects and this is done by set of lengthy structured interviews, however psychiatry study personality disorders based on mental feature like amygdale, fMRI etc. [10]. Graphology is still untouched to study personality disorders.…”

Section: Introductionmentioning

confidence: 99%

Identification of violent behavior using Handwriting

Jain¹

2020

IJATCSE

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Violent behavior in one's personality is hidden aspect as it has some roots in writer's subcoscious and is reflected in one's handwriting. Identifying violent behavior has its importance as it is closely related with personality disorders and its associated crimes. Identifying violence behavior in handwriting is crucial as it can prohibit future crimes. Graphology or handwriting analysis is a scientific method to identify, evaluate and understand the personality of an individual through patterns revealed by handwriting. Handwriting analysis is done by skilled graphologists however, with the advancement of technology we are able to facilitate the task of human specialists by processing handwritten images. This paper proposes a novel approach to identify violent and aggressive behavior through handwriting. The aim of this research is to study and examine the relationship between handwriting and violent behavior. The graphic handwriting characteristics of total 60 samples, were taken for the study including 30 for normal and 30 for violent handwriting samples. The samples of violent handwriting were analyzed and compared with normal handwriting samples. Some potential features in handwriting were found which were useful to identify violent and aggressive behavior.

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Detection of Deception Via Handwriting Behaviors Using a Computerized Tool: Toward an Evaluation of Malingering

Cited by 21 publications

References 21 publications

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Predicting the Big Five personality traits from handwriting

Identification of violent behavior using Handwriting

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