Bin-based weak classifier fusion of iris and face biometrics

Both high accuracy of iris biometrics and friendly interface of face recognition are important issues to a biometric recognition system. So an open problem is how to combine iris and face biometrics for reliable personal identification. This paper proposes a bin-based weak classifier fusion method for Multibiometrics of Iris and Face. The matching scores of iris and face image patches are partitioned into multiple bins so that the weak classifiers are learned on the bins. Such a non-linear score mapping is simple and efficient but it can discover detailed and distinctive information hidden in matching scores. So that pattern classification performance of the matching scores can be significantly improved. In addition, an ensemble learning method based on boosting is used to select the most discriminant and robust bin-based weak classifiers for identity verification. The excellent performance on the CASIA-Iris-Distance demonstrates the advantages of the proposed method over other multibiometric fusion methods.