Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
This work provides a non-commercial dataset to advance research in smartphone user authentication, addressing security needs for mobile device users, but it is incremental as it builds on existing authentication techniques.
The paper introduces the UMDAA-02 dataset for multi-modal user authentication on smartphones, focusing on front camera, touch sensor, and location service, and presents benchmark results showing that current methods need improvement to achieve satisfactory verification accuracy.
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.