Class imbalance poses serious difficulties to most standard two-class classifiers, when applied in performing classification in the context of multimodal biometric authentication. Most conventional classifiers assume equally balanced classes. They do not work well when impostor samples vastly outnumber the samples of the genuine user class. In this paper, we propose an algorithm, called RABOC, which inherits the natural capabilities of one-class classification and Real AdaBoost algorithm to handle the class imbalance problem in biometric systems. Particularly, we develop a weak classifier, which consists of one-class classifiers and is trained using data from both classes. We then exploit Real AdaBoost to combine the multiple weak classifiers in order to improve their performance without causing overfitting. Unlike the conventional Real AdaBoost, the weak classifiers in the proposed schema are learned on the same data set, but with different parameter choices. This not only generates the diversity necessary to make RABOC work, but also reduces the number of user-specified parameters. Extensive experiments were carried out on the BioSecure DS2 and XM2VTS benchmark databases, which involve data with extremely imbalanced class distribution. They demonstrate that the proposed RABOC algorithm can achieve a relative performance improvement of 28%, 24%, and 22% as compared to other state-of-the-art techniques, specifically the sum of scores, likelihood ratio based score fusion, and Support Vector Machines.
['Quang Duc Tran', 'Panos Liatsis']
RABOC: An approach to handle class imbalance in multimodal biometric authentication ☆