Demo Data from EDBT 2026 / reviewers in the wild / expert
Jonathan D. Stosz
dblp:317/4719
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1997
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Biometric security · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Biometric security › fingerprint recognition
fingerprint matching |
0.0 | 1 | 1997 | Fingerprint features-statistical analysis and system performance estimates · Proc. IEEE 1997 |
Biometric security
fingerprint recognition |
0.0 | 1 | 1997 | Fingerprint features-statistical analysis and system performance estimates · Proc. IEEE 1997 |
Biometric security › biometric performance evaluation
error rate estimation |
0.0 | 1 | 1997 | Fingerprint features-statistical analysis and system performance estimates · Proc. IEEE 1997 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 0.0performance modeling · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Fingerprint features-statistical analysis and system performance estimatesabstractAs the need for personal authentication increases, many people are turning to biometric authentication as an alternative to traditional security devices. Concurrently, users and vendors of biometric authentication systems are searching for methods to establish system performance. This paper presents a model that defines the parameters necessary to estimate the performance of fingerprint-authentication systems without going through the rigors of intensive system testing inherent in establishing error rates. The model presented here was developed to predict the performance of the pore-based automated fingerprint-matching routine developed internally in the research and development division at the National Security Agency. This paper also discusses the statistics of fingerprint pores and the efficacy of using pores in addition to the traditionally used minutiae to improve system performance. In addition this paper links together the realms of automated matching and statistical evaluations of fingerprint features. The results of this link provides knowledge of practical performance limits of any automated matching routine that utilizes pores or minutia features. Andrea R. Roddy, Jonathan D. Stosz |
Proc. IEEE | 2 |