Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation
Giuseppe Stragapede, Ruben Vera-Rodriguez, Ruben Tolosana, Aythami, Morales, Naser Damer, Julian Fierrez, Javier Ortega-Garcia

TL;DR
This paper introduces a comprehensive benchmarking framework for keystroke dynamics biometric verification, emphasizing fairness and enabling comparison across systems using a large, diverse dataset and novel metrics.
Contribution
It presents a new experimental framework and fairness metric for keystroke biometrics, facilitating standardized evaluation and addressing bias issues in the field.
Findings
The framework enables benchmarking on over 185,000 subjects.
Extended time-domain features maintain performance while enhancing privacy.
State-of-the-art systems show promising results with the new approach.
Abstract
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,…
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Taxonomy
TopicsUser Authentication and Security Systems · Hand Gesture Recognition Systems · Social Robot Interaction and HRI
