Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Combining verification decisions in a multi-vendor environment
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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Selecting appropriate thresholds and fusion rules for a system involving multiple biometric verifiers requires knowledge of the match score statistics for each verifier. While this statistical information can often be measured from training data, that data may not be representative of the environment into which each verifier is deployed. To compensate for missing statistics, we present a technique for estimating the error rates of each verifier using decisions made after a system has been deployed. While this post-deployment data lacks class labels, it is guaranteed to be representative. Extracted error rates can be used to select appropriate fusion rules and search for thresholds that meet operational requirements.