# BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation

arXiv: https://arxiv.org/abs/2206.02502 · HTML: https://mlsanity.com/paper/2206.02502/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations](https://mlsanity.com/paper/2009.06308/) (2020)
- [DeepSign: Deep On-Line Signature Verification](https://mlsanity.com/paper/2002.10119/) (2020)
- [BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks](https://mlsanity.com/paper/2001.10223/) (2020)
- [TypeNet: Deep Learning Keystroke Biometrics](https://mlsanity.com/paper/2101.05570/) (2021)
- [A Survey of Privacy Vulnerabilities of Mobile Device Sensors](https://mlsanity.com/paper/2106.10154/) (2021)
- [Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication](https://mlsanity.com/paper/2006.00790/) (2020)
- [SVC-onGoing: Signature Verification Competition](https://mlsanity.com/paper/2108.06090/) (2021)
- [Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results](https://mlsanity.com/paper/1610.07930/) (2016)
- [Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning](https://mlsanity.com/paper/1708.09754/) (2017)
- [Learning Human Identity from Motion Patterns](https://mlsanity.com/paper/1511.03908/) (2015)
- [Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features](https://mlsanity.com/paper/1901.05107/) (2019)
- [Mobile Behavioral Biometrics for Passive Authentication](https://mlsanity.com/paper/2203.07300/) (2022)
- [Exploring Ways To Mitigate Sensor-Based Smartphone Fingerprinting](https://mlsanity.com/paper/1503.01874/) (2015)

## Built on by (most cited first, 8 of 8)

- [TypeFormer: Transformers for Mobile Keystroke Biometrics](https://mlsanity.com/paper/2212.13075/) (2022)
- [IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)](https://mlsanity.com/paper/2210.03072/) (2022)
- [Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation](https://mlsanity.com/paper/2311.06000/) (2023)
- [Type2Branch: Keystroke Biometrics based on a Dual-branch Architecture with Attention Mechanisms and Set2set Loss](https://mlsanity.com/paper/2405.01088/) (2024)
- [IEEE BigData 2023 Keystroke Verification Challenge (KVC)](https://mlsanity.com/paper/2401.16559/) (2024)
- [KVC-onGoing: Keystroke Verification Challenge](https://mlsanity.com/paper/2412.20530/) (2024)
- [Free-text Keystroke Authentication using Transformers: A Comparative Study of Architectures and Loss Functions](https://mlsanity.com/paper/2310.11640/) (2023)
- [Telling Human and Machine Handwriting Apart](https://mlsanity.com/paper/2601.11700/) (2026)

## Abstract (arXiv)

Mobile behavioral biometrics have become a popular topic of research, reaching promising results in terms of authentication, exploiting a multimodal combination of touchscreen and background sensor data. However, there is no way of knowing whether state-of-the-art classifiers in the literature can distinguish between the notion of user and device. In this article, we present a new database, BehavePassDB, structured into separate acquisition sessions and tasks to mimic the most common aspects of mobile Human-Computer Interaction (HCI). BehavePassDB is acquired through a dedicated mobile app installed on the subjects' devices, also including the case of different users on the same device for evaluation. We propose a standard experimental protocol and benchmark for the research community to perform a fair comparison of novel approaches with the state of the art. We propose and evaluate a system based on Long-Short Term Memory (LSTM) architecture with triplet loss and modality fusion at score level.