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Touch keystroke dynamics for demographic classification
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AI
DOI：10.1016/j.patrec.2022.04.023.png)
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Abstract
En 中文
Soft biometric traits are not fully distinctive in recognition tasks, but they can effectively added to bio-metric recognition systems to improve the overall performance. In this work, the focus is on the analysis of touch keystroke dynamics of smartphone's users for demographic classification in age, gender and user experience. Starting from the data collected in three publicly available datasets and using traditional lightweight machine learning classification algorithms, the results reported in this work shows that an effective demographic analysis can be achieved as well as continuous authentication could be improved. Moreover, the study emphasize a critical issue affecting the experimental protocols in soft biometric anal-ysis, discussing how sensibly the performance of a system can increase on a not wise splitting of the samples in the datasets.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Soft biometrics
Touch dynamics
Demographic analysis
Machine learning
Classification
Soft biometrics
Touch dynamics
Demographic analysis
Machine learning
Classification
Journal
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