Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

TL;DR

This paper introduces a new multi-modal dataset for smartphone user authentication and provides benchmark results across various sensors, highlighting the need for more robust methods for accurate verification.

Contribution

It presents a unique non-commercial dataset for multi-modal user authentication research and benchmark results for several verification tasks on smartphones.

Findings

Face detection and verification results indicate room for improvement.

Touch-based identification shows moderate accuracy.

Location-based prediction requires more robust models.

Abstract

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.