Score-Level Multibiometric Fusion Based on Dempster–Shafer Theory Incorporating Uncertainty Factors

Abstract

While existing multibiometic Dempster-Shafer theory fusion approaches have demonstrated promising performance, they do not model the uncertainty appropriately, suggesting that further improvement can be achieved. This research seeks to develop a unified framework for multimodal biometric fusion to take advantage of the uncertainty concept of Dempster-Shafer theory, improving the performance of multibiometric authentication systems. Modeling uncertainty as a function of uncertainty factors affecting the recognition performance of the biometric systems helps to address the uncertainty of the data and the confidence of the fusion outcome. A weighted combination of quality measures and classifiers performance (equal error rate) is proposed to encode the uncertainty concept to improve the fusion. We also found that quality measures contribute unequally to the recognition performance; thus, selecting only significant factors and fusing them with a Dempster-Shafer approach to generate an overall quality score play an important role in the success of uncertainty modeling. The proposed approach achieved a competitive performance (approximate 1% EER) in comparison with other Dempster-Shafer-based approaches and other conventional fusion approaches.