Incremental granular relevance vector machine: A case study in multimodal biometrics

• The proposed iGRVM incorporates incremental and granular learning in RVM.
• Experiments are performed on NIST BSSR1, CASIA-Iris-Distance V4, and Biosecure DS2 databases.
• Results illustrate that iGRVM can be a good alternative for biometric score classification.

This paper focuses on extending the capabilities of relevance vector machine which is a probabilistic, sparse, and linearly parameterized classifier. It has been shown that both relevance vector machine and support vector machine have similar generalization performance but RVM requires significantly fewer relevance vectors. However, RVM has certain limitations which limits its applications in several pattern recognition problems including biometrics such as (1) slow training process, (2) difficult to train with large training samples, and (3) may not be suitable to handle large class imbalance. To address these limitations, we propose iGRVM which incorporates incremental and granular learning in RVM. The proposed classifier is evaluated in context to multimodal biometrics score classification using the NIST BSSR1, CASIA-Iris-Distance V4, and Biosecure DS2 databases. The experimental analysis illustrates that the proposed classifier can be a good alternative for biometric score classification with faster testing time.