Credence estimation and error prediction in biometric identity verification
This paper focuses on the estimation of credence in the correctness of classification decisions produced by a biometric identity verification system. We adopt the concept of decision credence defined in terms of subjective Bayesian degree of belief. We demonstrate how credence estimates can be used to predict verification errors and to rectify them, thus improving the classification performance. We also show how the framework of credence estimation helps handle erroneous classification decisions thanks to seamless incorporation of quality measures. Further, we demonstrate that credence information can be effectively applied to perform fusion of decisions in a multimodal scenario.