Deep Learning Approach in Predicting Personal Traits Based on the Way User Type on Touchscreen

Roy, Soumen; Roy, Utpal; Sinha, Devadatta

doi:10.1007/978-981-13-9042-5_27

“…Other than KD, touch dynamics was used for the first time to identify soft individuality of a person (age & gender) by applying deep learning (using deepnet package in R) along with Leave-one-user-out cross-validation scheme. Here in privately recorded dataset, imbalance was handled to maintain reliable results as: 98.74% and 88.08% accuracies respectively for age and gender recognition [19]. In comparison to other popular machine learning methods, current work exhibited a definite raise with 97% accuracy [20].…”

Section: Multilayer Feed-forward Neural Network (Mfnn)mentioning

confidence: 86%

Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

Tewari¹

2022

Preprint

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“…Other than KD, touch dynamics was used for the first time to identify soft individuality of a person (age & gender) by applying deep learning (using deepnet package in R) along with Leave-one-user-out cross-validation scheme. Here in privately recorded dataset, imbalance was handled to maintain reliable results as: 98.74% and 88.08% accuracies respectively for age and gender recognition [19]. In comparison to other popular machine learning methods, current work exhibited a definite raise with 97% accuracy [20].…”

Section: Multilayer Feed-forward Neural Network (Mfnn)mentioning

confidence: 86%

Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

Tewari¹

2022

Preprint

2

0

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“…On the other hand, age [28], gender [29], handedness [30], hand(s) used [31], neural stress [32], and education level [30] are all relevant information that may be determined for a number of fascinating applications. Since people generate millions of typing patterns each session, this might be a realistic technique to extract this useful and important information.…”

Section: Beyond Authentication Designmentioning

confidence: 99%

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

Roy

¹

,

Pradhan

²

,

Kumar

³

et al. 2022

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The purpose of this study is to conduct a comprehensive evaluation and analysis of the most recent studies on the implications of keystroke dynamics (KD) patterns in user authentication, identification, and the determination of useful information. Another aim is to provide an extensive and up-to-date survey of the recent literature and potential research directions to understand the present state-of-the-art methodologies in this particular domain that are expected to be beneficial for the KD research community. From January 1st, 2017 to March 13th, 2022, the popular six electronic databases have been searched using a search criterion ("keystroke dynamics" OR "typing pattern") AND ("authentication" OR "verification" OR "identification"). With this criterion, a total of nine thousand three hundred forty-eight results, including duplicates, were produced. However, one thousand five hundred forty-seven articles have been chosen after removing duplicates and preliminary screening. Due to insufficient information, only one hundred twentyseven high-quality quantitative research articles have been included in the article selection process. We compared and summarised several factors with multiple tables to comprehend the various methodologies, experimental settings, and findings. In this study, we have identified six unique KD-based designs and presented the status of findings toward an effective solution in authentication, identification, and prediction. We have also discovered considerable heterogeneity across studies in each KD-based design for desktops and smartphones separately. Finally, this paper found a few open research challenges and provided some indications for a deeper understanding of the issues and further study.

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Deep Learning Approach in Predicting Personal Traits Based on the Way User Type on Touchscreen

Cited by 4 publications

References 9 publications

Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

Keystroke Dynamics based Recognition Systems using Deep Learning: A Survey

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

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