FVC2002: Second Fingerprint Verification Competition

By Dario Maio, Davide Maltoni, Raffaele Cappelli, James L. Wayman, Anil K. Jain. Object recognition supported by user interaction for service robots, 2002

773 citations recorded by the source corpus.

Subfields: Information Systems, Safety Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0115c4bfde2456930f7b966ea2de4584199ae583

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Safety Research

In Signal Processing

Citations in the corpus

FVC2004: Third Fingerprint Verification Competition (2004, background): “This initiative follows FVC2000 [5], [2] and FVC2002 [6], [3], the first two international Fingerprint Verification Competitions, which were organized by the authors in the years 2000 and 2002 with results presented at the 15 ICPR and the 16 ICPR, respectively.”
Performance evaluation of fingerprint verification systems (2006, methodology): “Current scientific papers on fingerprint recognition now regularly report results using the publicly-available databases collected in our previous competitions [17], [18].”
Inference for empirical Wasserstein distances on finite spaces (2016, methodology): “In Section 3.2 we use this result to assess the statistical significance of the differences between real and synthetically generated fingerprints in the fingerprint verification competition (Maio et al., 2002).”
A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation (2019, methodology): “They used fingerprint features automatically extracted from the database FVC2002 DB1_A [103].”
Fingerprint Quality Indices for Predicting Authentication Performance (2005, methodology): “The quality indices are tested using two databases, namely the IBM-HURSLEY database and FVC2002 DB3 [11].”
Fingerprint-Based Fuzzy Vault: Implementation and Performance (2007, background): “Apart from the location and orientation attributes of a minutia point, many minutiae-based fingerprint matchers use additional attributes, such as minutia type, ridge counts, ridge curvature, and ridge density to achieve high recognition rates.”
Biometric Template Security (2008, methodology): “This database [87] consists of 800 images of 100 fingers with 8 impressions per finger obtained using an optical sensor.”