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Online Learning in Biometrics: A Case Study in Face Classifier Update

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Executive Summary

In large scale applications, hundreds of new subjects may be regularly enrolled in a biometric system. To account for the variations in data distribution caused by these new enrollments, biometric systems require regular re-training which usually results in a very large computational overhead. This paper formally introduces the concept of online learning in biometrics. This paper demonstrates its application in classifier update algorithms to re-train classifier decision boundaries.

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