On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment
This work addresses privacy and security concerns by enabling user profiling from typing data in multi-device environments, though it is incremental as it benchmarks existing methods on new data.
The paper tackled the problem of inferring soft biometrics like gender, major, typing style, age, and height from typing patterns across multiple devices, achieving accuracies up to 96.15% for typing style and mean absolute errors as low as 1.77 years for age.
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.