Template Ageing and Longitudinal Verification in Fixed-Text Keystroke Dynamics: A Subject-Disjoint Study Across Eight Weeks
Eight-week keystroke study finds fixed-text biometric templates age about 1.7 error points per week across four matchers.
A subject-disjoint study measured template ageing in fixed-text keystroke dynamics using 40 passwords typed four times in each weekly session for eight weeks. Researchers compared a scaled-Manhattan matcher, a gradient-boosted classifier, a TypeNet-style recurrent model, and a TypeFormer-style Transformer across enrolment-to-query gaps of zero to seven weeks. Equal error rates rose for every matcher, from 14.6–27.2% at no gap to 25.5–37.1% at seven weeks, about 1.7 percentage points per week. Baseline accuracy spanned 12.6 points, more than the 2.3-point spread in accumulated ageing, and ageing never reordered the matchers.
- Forty fixed passwords were typed four times weekly for eight weeks.
- Equal error rate rose from 14.6–27.2% at no gap to 25.5–37.1% at seven weeks.
- Error grew about 1.7 points per week for every matcher.
- Matcher accuracy differed more than ageing rate and rankings never changed.
- TypeNet-style M3 was least accurate but aged slower than scaled Manhattan.
- AI models
- TypeNetTypeFormer
From the original article
1 min read at arxiv.orgBehavioural biometric templates are widely believed to degrade as the gap between enrolment and verification grows, but few studies measure this template ageing effect directly under controlled conditions. We collected a longitudinal dataset of 40 fixed passwords, each typed four times per weekly session over eight consecutive weeks.
By Simon Parkinson · arxiv.org
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