Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Performance generalization in biometric authentication using joint user-specific and sample bootstraps.

Norman Poh1, Alvin Martin, Samy Bengio

  • 1IDIAP Research Institute, Martigny, Switzerland. norman@idiap.ch

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 17, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition.

Machine vision and applications·2020
Same author

Avoiding overstating the strength of forensic evidence: Shrunk likelihood ratios/Bayes factors.

Science & justice : journal of the Forensic Science Society·2018
Same author

Probabilistic broken-stick model: A regression algorithm for irregularly sampled data with application to eGFR.

Journal of biomedical informatics·2017
Same author

Blood transcriptome based biomarkers for human circadian phase.

eLife·2017
Same author

Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge.

IEEE transactions on pattern analysis and machine intelligence·2017
Same author

Improving the measurement of longitudinal change in renal function: automated detection of changes in laboratory creatinine assay.

Journal of innovation in health informatics·2015
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Detection error trade-off (DET) curves for biometric authentication depend on database specifics. A new bootstrap method improves prediction of unseen DET curves with more users and data.

Area of Science:

  • Computer Science
  • Biometrics
  • Machine Learning

Background:

  • Detection error trade-off (DET) curves are standard for visualizing biometric authentication performance.
  • Existing methods for evaluating DET curves are sensitive to database characteristics like sample selection, demographics, and user count.

Purpose of the Study:

  • To develop a robust method for predicting biometric authentication performance (DET curves) that accounts for database variability.
  • To enhance the reliability of DET curve analysis by incorporating sample selection, demographic composition, and user number.

Main Methods:

  • A novel two-step bootstrap procedure was proposed, extending Bolle et al.'s technique.
  • The method addresses variability from sample choice, demographic composition, and the number of users in a biometric database.

Related Experiment Videos

  • Experiments were conducted on the NIST2005 and XM2VTS benchmark databases.

    • Main Results:

    • Preliminary experiments show encouraging results, with an average of over 75 percent DET coverage when predicting unseen DET curves with eight times more users on NIST2005.
    • The proposed bootstrap procedure effectively accounts for the specified sources of variability.
    • Increased data availability leads to smaller and more informative confidence intervals for DET curves.

    Conclusions:

    • The proposed two-step bootstrap procedure offers a more reliable way to assess and predict biometric authentication performance.
    • This method enhances the generalizability of DET curve analysis across different database compositions.
    • The findings suggest improved confidence in performance predictions as more data becomes available.