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SwipeFormer: Transformers for mobile touchscreen biometrics

Delgado-Santos, Paula, Tolosana, Ruben, Guest, Richard, Lamb, Parker, Khmelnitsky, Andrei, Coughlan, Colm, Fierrez, Julian (2023) SwipeFormer: Transformers for mobile touchscreen biometrics. Expert Systems with Applications, 237 (Part C). Article Number 121537. ISSN 0957-4174. (doi:10.1016/j.eswa.2023.121537) (KAR id:103038)

Abstract

The growing number of mobile devices over the past few years brings a large amount of personal information, which needs to be properly protected. As a result, several mobile authentication methods have been developed. In particular, behavioural biometrics has become one of the most relevant methods due to its ability to extract the uniqueness of each subject in a secure, non-intrusive, and continuous way. This article presents SwipeFormer, a novel Transformer-based system for mobile subject authentication by means of swipe gestures in an unconstrained scenario (i.e., subjects could use their personal devices freely, without restrictions on the direction of swipe gestures or the position of the device). Our proposed system contains two modules: (i) a Transformer-based feature extractor, and (ii) a similarity computation module. Mobile data from the touchscreen and different background sensors (accelerometer and gyroscope) have been studied, including in the analysis both Android and iOS operating systems. A complete analysis of SwipeFormer is carried out using an in-house large-scale database acquired in unconstrained scenarios. In these operational conditions, SwipeFormer achieves Equal Error Rate (EER) values of 6.6% and 3.6% on Android and iOS respectively, outperforming the state of the art. In addition, we evaluate SwipeFormer on the popular publicly available databases Frank DB and HuMIdb, achieving EER values of 11.0% and 5.0% respectively, outperforming previous approaches under the same experimental setup.

Item Type: Article
DOI/Identification number: 10.1016/j.eswa.2023.121537
Uncontrolled keywords: Behavioural biometrics; Touchscreen; Swipe verification; Transformers; Deep learning; Mobile devices
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: European Union (https://ror.org/019w4f821)
Comunidad de Madrid (https://ror.org/040scgh75)
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 27 Oct 2023 14:12 UTC
Last Modified: 11 Jan 2024 06:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/103038 (The current URI for this page, for reference purposes)

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