# BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2001.10223 · HTML: https://mlsanity.com/paper/2001.10223/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [TypeNet: Deep Learning Keystroke Biometrics](https://mlsanity.com/paper/2101.05570/) (2021)
- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [SVC-onGoing: Signature Verification Competition](https://mlsanity.com/paper/2108.06090/) (2021)
- [An Overview of Privacy-enhancing Technologies in Biometric Recognition](https://mlsanity.com/paper/2206.10465/) (2022)
- [Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection](https://mlsanity.com/paper/2109.05701/) (2021)
- [ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation](https://mlsanity.com/paper/2204.03992/) (2022)
- [Exploring Transformers for Behavioural Biometrics: A Case Study in Gait Recognition](https://mlsanity.com/paper/2206.01441/) (2022)
- [ICDAR 2021 Competition on On-Line Signature Verification](https://mlsanity.com/paper/2106.00739/) (2021)
- [BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation](https://mlsanity.com/paper/2206.02502/) (2022)
- [MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification](https://mlsanity.com/paper/2210.08836/) (2022)
- [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)
- [Type2Branch: Keystroke Biometrics based on a Dual-branch Architecture with Attention Mechanisms and Set2set Loss](https://mlsanity.com/paper/2405.01088/) (2024)
- [Mobile Behavioral Biometrics for Passive Authentication](https://mlsanity.com/paper/2203.07300/) (2022)
- [Privacy-Preserving Biometric Verification with Handwritten Random Digit String](https://mlsanity.com/paper/2503.12786/) (2025)
- [Exploring Transformers for On-Line Handwritten Signature Verification](https://mlsanity.com/paper/2307.01663/) (2023)
- [Writer Recognition Using Off-line Handwritten Single Block Characters](https://mlsanity.com/paper/2201.10665/) (2022)
- [Introduction to Presentation Attacks in Signature Biometrics and Recent Advances](https://mlsanity.com/paper/2302.08320/) (2023)
- [Capturing More: Learning Multi-Domain Representations for Robust Online Handwriting Verification](https://mlsanity.com/paper/2508.01427/) (2025)

## Abstract (arXiv)

Passwords are still used on a daily basis for all kind of applications. However, they are not secure enough by themselves in many cases. This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. The main contributions of this study are as follows: i) We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. This database contains more than 64K on-line character samples performed by 217 users, with 94 different smartphone models, and up to 6 acquisition sessions. ii) We perform a complete analysis of the proposed approach considering both traditional authentication systems such as Dynamic Time Warping (DTW) and novel approaches based on Recurrent Neural Networks (RNNs). In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs). This approach combines the potential of DTW and RNNs to train more robust systems against attacks. A complete analysis of the proposed approach is carried out using both MobileTouchDB and e-BioDigitDB databases. Our proposed TA-RNN system outperforms the state of the art, achieving a final 2.38% Equal Error Rate, using just a 4-digit password and one training sample per character. These results encourage the deployment of our proposed approach in comparison with traditional typed-based password systems where the attack would have 100% success rate under the same impostor scenario.