Online Learning in Biometrics: A Case Study in Face Classifier Update
Source: West Virginia University
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.