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

arXiv: https://arxiv.org/abs/1610.07930 · HTML: https://mlsanity.com/paper/1610.07930/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2016.

## Built on by (most cited first, 10 of 10)

- [BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation](https://mlsanity.com/paper/2206.02502/) (2022)
- [Federated Learning-based Active Authentication on Mobile Devices](https://mlsanity.com/paper/2104.07158/) (2021)
- [Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks](https://mlsanity.com/paper/2106.07867/) (2021)
- [Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication](https://mlsanity.com/paper/2211.05662/) (2022)
- [GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System](https://mlsanity.com/paper/2210.01594/) (2022)
- [CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication](https://mlsanity.com/paper/2405.07174/) (2024)
- [Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios](https://mlsanity.com/paper/2503.23930/) (2025)
- [Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles](https://mlsanity.com/paper/2105.09900/) (2021)
- [An Omnidirectional Approach to Touch-based Continuous Authentication](https://mlsanity.com/paper/2302.08498/) (2023)
- [Robust Face Liveness Detection for Biometric Authentication using Single Image](https://mlsanity.com/paper/2511.02645/) (2025)

## Abstract (arXiv)

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.