Mobile Behavioral Biometrics for Passive Authentication

TL;DR

This paper evaluates passive mobile user authentication using behavioral biometrics, combining touchscreen and sensor data with neural networks, achieving low error rates in a comprehensive database.

Contribution

It provides a comparative analysis of unimodal and multimodal behavioral biometric traits for passive authentication on mobile devices.

Findings

Magnetometer is the most discriminative sensor.

Keystroke tasks yield the best results in fixed-text scenarios.

Multimodal fusion significantly improves authentication accuracy.

Abstract

Current mobile user authentication systems based on PIN codes, fingerprint, and face recognition have several shortcomings. Such limitations have been addressed in the literature by exploring the feasibility of passive authentication on mobile devices through behavioral biometrics. In this line of research, this work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits acquired while the subjects perform different activities on the phone such as typing, scrolling, drawing a number, and tapping on the screen, considering the touchscreen and the simultaneous background sensor data (accelerometer, gravity sensor, gyroscope, linear accelerometer, and magnetometer). Our experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases to date. A separate Recurrent Neural Network (RNN) with triplet loss is implemented for each single modality. Then, the weighted fusion of the different modalities is carried out at score level. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke in a fixed-text scenario. In all cases, the fusion of modalities is very beneficial, leading to Equal Error Rates (EER) ranging from 4% to 9% depending on the modality combination in a 3-second interval.