Maximizing area under ROC curve for biometric scores fusion

DOI10.1016/j.patcog.2008.04.002zbMath1154.68502OpenAlexW2024206313MaRDI QIDQ941583

Kar-Ann Toh, Jaihie Kim, Sang-Youn Lee

Publication date: 1 September 2008

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2008.04.002

zbMATH Keywords

pattern classificationmachine learningbiometricsdecision Fusionreceiver operating characteristics

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (7)

A feature extraction method for use with bimodal biometrics ⋮ Optimizing between data transformation and parametric weighting for stable binary classification ⋮ Optimizing area under the ROC curve using semi-supervised learning ⋮ An online AUC formulation for binary classification ⋮ ROC curves for regression ⋮ Exploiting the relationships among several binary classifiers via data transformation ⋮ Consistency of the estimator of binary response models based on AUC maximization

Uses Software

• HandTill2001
• OSU SVM

Cites Work

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• Training a reciprocal-sigmoid classifier by feature scaling-space
• The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
• Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge
• Handbook of Face Recognition
• Handbook of Fingerprint Recognition
• 10.1162/1532443041827916
• Robust classification for imprecise environments
• A simple generalisation of the area under the ROC curve for multiple class classification problems
• Decision templates for multiple classifier fusion: An experimental comparison

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