# On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment

arXiv: https://arxiv.org/abs/2006.09501 · HTML: https://mlsanity.com/paper/2006.09501/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Keystroke Dynamics Against Academic Dishonesty in the Age of LLMs](https://mlsanity.com/paper/2406.15335/) (2024)
- [Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks](https://mlsanity.com/paper/2106.07867/) (2021)

## Abstract (arXiv)

In this paper, we study the inference of gender, major/minor (computer science, non-computer science), typing style, age, and height from the typing patterns collected from 117 individuals in a multi-device environment. The inference of the first three identifiers was considered as classification tasks, while the rest as regression tasks. For classification tasks, we benchmark the performance of six classical machine learning (ML) and four deep learning (DL) classifiers. On the other hand, for regression tasks, we evaluated three ML and four DL-based regressors. The overall experiment consisted of two text-entry (free and fixed) and four device (Desktop, Tablet, Phone, and Combined) configurations. The best arrangements achieved accuracies of 96.15%, 93.02%, and 87.80% for typing style, gender, and major/minor, respectively, and mean absolute errors of 1.77 years and 2.65 inches for age and height, respectively. The results are promising considering the variety of application scenarios that we have listed in this work.