1 research outputs found
On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment
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.Comment: The first two authors contributed equally. The code is available upon
request. Please contact the last autho