<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Biometric systems aim to provide reliable authentication and verification of users. The behaviour of the users may alter the authentication performance when accessing these systems. Therefore, clustering users based on their actions is crucial. A biometric menagerie defines and labels user groups statistically according to their variability. However, determining groups is a fuzzy process and it may lead to inconsistencies. In this work, a novel and flexible approach is introduced based on the classification performance of the users data collected in a database without imposing any other restrictions. According to the performance measures obtained from the confusion matrix of the classification algorithms, users are ranked and then clustered. Additionally, the norm of a confusion matrix is offered augmenting the state-of-the-art performance metrics. The proposed scheme is evaluated using the behavioural biometrics modality on two benchmark keystroke databases. The performance results successfully illustrate the alternative way of grouping and identification of users sharing the same behaviour irrespective of the chosen classifiers or performance metrics.</p>
</abstract>

Biometric systems aim to provide reliable authentication and verification of users. The behaviour of the users may alter the authentication performance when accessing these systems. Therefore, clustering users based on their actions is crucial. A biometric menagerie defines and labels user groups statistically according to their variability. However, determining groups is a fuzzy process and it may lead to inconsistencies. In this work, a novel and flexible approach is introduced based on the classification performance of the users data collected in a database without imposing any other restrictions. According to the performance measures obtained from the confusion matrix of the classification algorithms, users are ranked and then clustered. Additionally, the norm of a confusion matrix is offered augmenting the state-of-the-art performance metrics. The proposed scheme is evaluated using the behavioural biometrics modality on two benchmark keystroke databases. The performance results successfully illustrate the alternative way of grouping and identification of users sharing the same behaviour irrespective of the chosen classifiers or performance metrics.

A flexible approach for biometric menagerie on user classification of keystroke data

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Biometric systems aim to provide reliable authentication and verification of users. The behaviour of the users may alter the authentication performance when...

A flexible approach for biometric menagerie on user classification of keystroke data

Article Category: PAPERS

Published Online: Mar 07, 2023

Page range: 23 - 31

Received: Jan 24, 2023

DOI: https://doi.org/10.2478/jee-2023-0003

Keywords
biometric menagerie, confusion matrix, keystroke dynamics

© 2023 Mehmet Erdal Özbek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A flexible approach for biometric menagerie on user classification of keystroke data

Article Category: PAPERS

Published Online: Mar 07, 2023

Page range: 23 - 31

Received: Jan 24, 2023

DOI: https://doi.org/10.2478/jee-2023-0003

Keywordsbiometric menagerie, confusion matrix, keystroke dynamics

© 2023 Mehmet Erdal Özbek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
biometric menagerie, confusion matrix, keystroke dynamics