  Analyzing keystroke dynamics (KD) for biometric verification has several
advantages: it is among the most discriminative behavioral traits; keyboards
are among the most common human-computer interfaces, being the primary means
for users to enter textual data; its acquisition does not require additional
hardware, and its processing is relatively lightweight; and it allows for
transparently recognizing subjects. However, the heterogeneity of experimental
protocols and metrics, and the limited size of the databases adopted in the
literature impede direct comparisons between different systems, thus
representing an obstacle in the advancement of keystroke biometrics. To
alleviate this aspect, we present a new experimental framework to benchmark
KD-based biometric verification performance and fairness based on tweet-long
sequences of variable transcript text from over 185,000 subjects, acquired
through desktop and mobile keyboards, extracted from the Aalto Keystroke
Databases. The framework runs on CodaLab in the form of the Keystroke
Verification Challenge (KVC). Moreover, we also introduce a novel fairness
metric, the Skewed Impostor Ratio (SIR), to capture inter- and
intra-demographic group bias patterns in the verification scores. We
demonstrate the usefulness of the proposed framework by employing two
state-of-the-art keystroke verification systems, TypeNet and TypeFormer, to
compare different sets of input features, achieving a less privacy-invasive
system, by discarding the analysis of text content (ASCII codes of the keys
pressed) in favor of extended features in the time domain. Our experiments show
that this approach allows to maintain satisfactory performance.
