Modeling the Complexity of Signature and Touch-Screen Biometrics using the Lognormality Principle

Vera-Rodríguez, Rubén; Tolosana, Rubén; Hernandez-Ortega, Javier; Acien, Alejandro; Morales, Aythami; Fiérrez, Julián; Ortega‐Garcia, Javier

doi:10.1142/9789811226830_0003

Cited by 11 publications

(11 citation statements)

References 18 publications

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“…Most of the remaining studies consider the lognormal and other related distributions in a more general setting that includes not only keystroke dynamics but also touch-screen biometrics [27]. Going beyond authentication, [28] and [29] employ the sigma-lognormal model of rapid human movements to detect the age group of users based on their interaction with a touch screen, while [30] leverages different distributions to discriminate a human user from a bot.…”

Section: Previous Studiesmentioning

confidence: 99%

On the shape of timings distributions in free-text keystroke dynamics profiles

González

Calot

Ierache

et al. 2021

Heliyon

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Keystroke dynamics is a soft biometric trait. Although the shape of the timing distributions in keystroke dynamics profiles is a central element for the accurate modeling of the behavioral patterns of the user, a simplified approach has been to presuppose normality. Careful consideration of the individual shapes for the timing models could lead to improvements in the error rates of current methods or possibly inspire new ones. The main objective of this study is to compare several heavy-tailed and positively skewed candidate distributions in order to rank them according to their merit for fitting timing histograms in keystroke dynamics profiles. Results are summarized in three ways: counting how many times each candidate distribution provides the best fit and ranking them in order of success, measuring average information content, and ranking candidate distributions according to the frequency of hypothesis rejection with an Anderson-Darling goodness of fit test. Seven distributions with two parameters and seven with three were evaluated against three publicly available free-text keystroke dynamics datasets. The results confirm the established use in the research community of the log-normal distribution, in its two-and three-parameter variations, as excellent choices for modeling the shape of timings histograms in keystroke dynamics profiles. However, the log-logistic distribution emerges as a clear winner among all two-and three-parameter candidates, consistently surpassing the log-normal and all the rest under the three evaluation criteria for both hold and flight times.

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Section: Previous Studiesmentioning

confidence: 99%

On the shape of timings distributions in free-text keystroke dynamics profiles

González

Calot

Ierache

et al. 2021

Heliyon

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“…This research line has many different potential applications, e.g., restrict the access to adult contents or services such as on-line shopping. In [9], the authors presented an automatic system able to detect children from adults with classification rates over 96%. This detection system is based on the combination of features based on neuromotor skills, task time, and accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…In [20], Remi et al studied the scribbling activities executed by children of 3-6 years. They considered the Sigma-Lognormal writing generation model [9], [28] to analyse the motor skills, concluding that there are significant differences in the model parameters between ages. Stylus has also been considered in [26] to analyse the correlation between the performance of polygonal shape drawing and the levels in handwriting performance.…”

Section: Related Workmentioning

confidence: 99%

“…This section analyses quantitatively one of the many different potential applications of ChildCIdb. In particular, we focus on the popular task of children age group detection based on the interaction with mobile devices [7], [9], [10], [33]. Due to the large volume of information captured in ChildCIdb, we focus in this section only on the analysis of the Test 6 (Drawing Test) based on the way children colour a tree.…”

Section: Example Application: Age Detectionmentioning

confidence: 99%

“…To the best of our knowledge, this is the first study that focuses on the classification of children age groups (from 18 months to 8 years) based on the interaction with mobile devices. Previous studies were focused on a simpler task, i.e., classification between children (3-6 years) and adults [7], [9], [10], achieving in the best cases classification accuracy results of 96.3%. Comparing that result achieved in a simpler task with the results achieved in the present study (accuracy results over 90%), we can conclude that: i) good results are achieved, proving the soundness of the proposed age group classification systems, and ii) the possibility to distinguish with high-accuracy results between different children age groups.…”

Section: B Age Group Detection Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Child-Computer Interaction with Mobile Devices: Recent Works, New Dataset, and Age Detection

Tolosana,

Ruiz-Garcia,

Vera-Rodriguez

et al. 2021

Preprint

Self Cite

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We overview recent research in Child-Computer Interaction and describe our framework ChildCI intended for: i) generating a better understanding of the cognitive and neuromotor development of children while interacting with mobile devices, and ii) enabling new applications in e-learning and ehealth, among others. Our framework includes a new mobile application, specific data acquisition protocols, and a first release of the ChildCI dataset (ChildCIdb v1), which is planned to be extended yearly to enable longitudinal studies. In our framework children interact with a tablet device, using both a pen stylus and the finger, performing different tasks that require different levels of neuromotor and cognitive skills. ChildCIdb comprises more than 400 children from 18 months to 8 years old, considering therefore the first three development stages of the Piaget's theory. In addition, and as a demonstration of the potential of the ChildCI framework, we include experimental results for one of the many applications enabled by ChildCIdb: children age detection based on device interaction. Different machine learning approaches are evaluated, proposing a new set of 34 global features to automatically detect age groups, achieving accuracy results over 90% and interesting findings in terms of the type of features more useful for this task.

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