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. We demonstrate its application in classifier update algorithms to re-train classifier decision boundaries. Specifically, the algorithm employs online learning technique in a 2v-GranuIar Soft Support Vector Machine for rapidly training and updating face recognition systems. The proposed online classifier is used in a face recognition application for classifying genuine and impostor match scores impacted by different covariates. Experiments on a heterogeneous face database of 1,194 subjects show that the proposed online classifier not only improves the verification accuracy but also significantly reduces the computational cost.
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