Exploring gender prediction from digital handwriting

Erbilek, Meryem; Fairhurst, Michael; Li, Cheng

doi:10.1109/siu.2016.7495858

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…The database used oh this work was collected and first studied in [1]- [3]. This database is divided in two parts: the keystroke and the handwriting data.…”

Section: Behavioural Bimodal Dataset: Keystroke Dynamics and Hanmentioning

confidence: 99%

Investigating the Impact of Combining Handwritten Signature and Keyboard Keystroke Dynamics for Gender Prediction

Bandeira

Canuto

Abreu

et al. 2019

2019 8th Brazilian Conference on Intelligent Systems (BRACIS)

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The use of soft biometrics as an auxiliary tool on user identification is already well known. It is not, however, the only use possible for biometric data, as such data can be adequate to get low level information from the user that are not only related to his identity. Gender, hand-orientation and emotional state are some examples, which it can be called soft-biometrics. It is very common to find work using physiologic modalities for softbiometric prediction, but the behavioural data is often neglected. Two possible behavioural modalities that are not often found in the literature are keystroke dynamics and handwriting signature, which can be seen used alone to predict the users gender, but not in any kind of combination scenario. In order to fill this space, this study aims to investigate whether the combination of those two different biometric modalities can impact the gender prediction accuracy, and how this combination should be done.

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“…The database used oh this work was collected and first studied in [1]- [3]. This database is divided in two parts: the keystroke and the handwriting data.…”

Section: Behavioural Bimodal Dataset: Keystroke Dynamics and Hanmentioning

confidence: 99%

Investigating the Impact of Combining Handwritten Signature and Keyboard Keystroke Dynamics for Gender Prediction

Bandeira

Canuto

Abreu

et al. 2019

2019 8th Brazilian Conference on Intelligent Systems (BRACIS)

Self Cite

View full text Add to dashboard Cite

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“…In [162], the authors use a mobile phone platform to detect user emotion via data acquired from the user's social media applications. In Fairhurst et al [158], the proposed technique of using keystroke dynamics to detect a student's mental state, and Erbilek et al [163] suggests some important potential for predictive biometrics in healthcare applications.…”

Section: Prediction Of Higher Level Individual Characteristicsmentioning

confidence: 99%

“…[158], the proposed technique of using keystroke dynamics to detect a student's mental state, and Erbilek et al . [163] suggests some important potential for predictive biometrics in healthcare applications.…”

Section: Prediction From Biometric Datamentioning

confidence: 99%

Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data

Fairhurst

Abreu

2017

IET biom.

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Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, 'higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future.

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“…Therefore, analysis of one's handwriting and signature can reveal some information about the writer, such as behavioral traits, character or personality [38]. The handwriting and signature of individual can be measured and analyzed, therefore its measurability gives rise to opportunities for researchers to identify the identity of an individual as well as to predict their soft-biometric treats such as emotions [35], gender [39], age group [40], and assess the effects of alcohol [41].…”

Section: Introductionmentioning

confidence: 99%

Emotional State Prediction From Online Handwriting and Signature Biometrics

2019

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Handwriting and signature biometrics have a long history in the literature, especially in terms of identity recognition and/or verification; nevertheless, it reveals more information therefore provides more opportunities for personal characteristics estimation, particularly, emotional state. However, almost all publicly or commercially available databases do not include contributors' demographic labels or emotional status in addition to their identification labels, leading these available databases to be only useful for verification and identification based research studies. For this reason, this paper proposes both offline and online handwriting and signature biometric database with a wide range of ground truths (emotional status labels-happy, sad and stress) in addition to the identity labels and tries to predict ones' emotional state, namely happy, sad and stress, from their online biometric handwriting and signatures. The proposed database comprised total of 134 participants with 804 handwriting and 8040 signature biometric samples. The database presented also includes individuals' demographic information such as age, gender, handedness, education level and nationality. Subsequently, there are several experiments have been conducted, with different thresholds, which present the usability of the proposed database and preliminary results of emotional state prediction from both signature and handwriting biometrics. The experiments achieved remarkable success especially on stress prediction for handwriting. On the other hand, considering the results from signature biometrics, it is observed that happy and then sad and finally stress class forms the most part of the prediction accuracy.

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Exploring gender prediction from digital handwriting

Cited by 8 publications

References 18 publications

Investigating the Impact of Combining Handwritten Signature and Keyboard Keystroke Dynamics for Gender Prediction

Investigating the Impact of Combining Handwritten Signature and Keyboard Keystroke Dynamics for Gender Prediction

Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data

Emotional State Prediction From Online Handwriting and Signature Biometrics

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