End-to-end User Recognition using Touchscreen Biometrics

TL;DR

This paper presents an end-to-end deep learning system that identifies users based on raw touchscreen data, achieving high accuracy without manual feature extraction.

Contribution

It introduces a novel deep neural network approach that directly processes raw touchscreen data for user recognition, outperforming existing methods.

Findings

Achieved 0.65% EER in user identification

High accuracy demonstrated on touchscreen biometric data

Outperforms state-of-the-art systems in EER

Abstract

We study the touchscreen data as behavioural biometrics. The goal was to create an end-to-end system that can transparently identify users using raw data from mobile devices. The touchscreen biometrics was researched only few times in series of works with disparity in used methodology and databases. In the proposed system data from the touchscreen goes directly, without any processing, to the input of a deep neural network, which is able to decide on the identity of the user. No hand-crafted features are used. The implemented classification algorithm tries to find patterns by its own from raw data. The achieved results show that the proposed deep model is sufficient enough for the given identification task. The performed tests indicate high accuracy of user identification and better EER results compared to state of the art systems. The best result achieved by our system is 0.65% EER.