On Co-Training Online Biometric Classifiers
In an operational biometric verification system, changes in biometric data over a period of time can affect the classification accuracy. Online learning has been used for updating the classifier decision boundary. However, this requires labeled data that is only available during new enrollments. This paper presents a biometric classifier update algorithm in which the classifier decision boundary is updated using both labeled enrollment instances and unlabeled probe instances. The proposed co-training online classifier update algorithm is presented as a semi-supervised learning task and is applied to a face verification application. Experiments indicate that the proposed algorithm improves the performance both in terms of classification accuracy and computational time.