NeuroIDBench: An Open-Source Benchmark Framework for the Standardization of Methodology in Brainwave-based Authentication Research
arxiv(2024)
摘要
Biometric systems based on brain activity have been proposed as an
alternative to passwords or to complement current authentication techniques. By
leveraging the unique brainwave patterns of individuals, these systems offer
the possibility of creating authentication solutions that are resistant to
theft, hands-free, accessible, and potentially even revocable. However, despite
the growing stream of research in this area, faster advance is hindered by
reproducibility problems. Issues such as the lack of standard reporting schemes
for performance results and system configuration, or the absence of common
evaluation benchmarks, make comparability and proper assessment of different
biometric solutions challenging. Further, barriers are erected to future work
when, as so often, source code is not published open access. To bridge this
gap, we introduce NeuroIDBench, a flexible open source tool to benchmark
brainwave-based authentication models. It incorporates nine diverse datasets,
implements a comprehensive set of pre-processing parameters and machine
learning algorithms, enables testing under two common adversary models (known
vs unknown attacker), and allows researchers to generate full performance
reports and visualizations. We use NeuroIDBench to investigate the shallow
classifiers and deep learning-based approaches proposed in the literature, and
to test robustness across multiple sessions. We observe a 37.6
Equal Error Rate (EER) for unknown attacker scenarios (typically not tested in
the literature), and we highlight the importance of session variability to
brainwave authentication. All in all, our results demonstrate the viability and
relevance of NeuroIDBench in streamlining fair comparisons of algorithms,
thereby furthering the advancement of brainwave-based authentication through
robust methodological practices.
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