Performance evaluation of fingerprint verification systems

By Raffaele Cappelli, Dario Maio, Davide Maltoni, James L. Wayman, Anil K. Jain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

404 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6b06175651fcf34e2ba3d298ff0387b47cab1d06

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Citations in the corpus

Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition (2010, methodology): “Most of the fingerprint matching algorithms recently proposed exploit several extra features besides minutiae; in [34], some statistics about the features used by FVC2004 participants are reported.”
Fingerprint Image Reconstruction from Standard Templates (2007, background): “To the best of our knowledge, all the eight commercial algorithms (whose implementation details are industrial secrets) use minutiae as the main feature; on the other hand, it is likely that they also exploit other features to improve the performance [9].”
An evaluation of indirect attacks and countermeasures in fingerprint verification systems (2011, background): “Corroborating the increasing interest in Match-on-Card systems, in the Fingerprint Verification Competition (FVC) 2004 (Cappelli et al., 2006), a special evaluation track was introduced for the case of matching systems with reduced memory and time restrictions.”
Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol (2009, neutral): “The Fingerprint Vendor Technology Evaluation (FpVTE) [8] and the work by Cappelli et al. [9] evaluated fingerprint matching, identification, and verification systems with a goal of establishing a new common benchmark for an unambiguous comparison of fingerprint-based biometric systems.”
Biometric Authentication: A Review (2009, background): “Correlation based techniques require the precise location of a registration point and are affected by image translation and rotation [5, 6, 7, 8].”
Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living (2019, background): “Fingerprints are effective but are far from perfect—with EER values in the 2%-7% range [17].”
Behavioural biometrics: a survey and classification (2008, background): “The majority of research is aimed at studying well-established physical biometrics such as fingerprint (Cappelli et al,, 2006) or iris scans (Jain et al., 2004d).”