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

TL;DR

This study investigates how gender, age, height, and other attributes can be inferred from typing patterns across multiple devices using machine learning, achieving high accuracy and low error rates.

Contribution

It introduces a comprehensive evaluation of ML and DL methods for inferring soft biometrics from multi-device typing data, with detailed benchmarking.

Findings

High accuracy in classifying typing style, gender, and major/minor.

Low mean absolute errors for age and height predictions.

Effective across various device and text-entry configurations.

Abstract

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.