Semiparametric Likelihood-ratio-based Biometric Score Level Fusion via Parametric Copula

We present a mathematical framework for modelling dependence between biometric comparison scores in likelihood-based fusion by copula models. The pseudo-maximum likelihood estimator (PMLE) for the copula parameters and its asymptotic performance are studied. For a given objective performance measure in a realistic scenario, a resampling method for choosing the best copula pair is proposed. Finally, the proposed method is tested on some public biometric databases from fingerprint, face, speaker, and video-based gait recognitions under some common objective performance measures: maximizing acceptance rate at fixed false acceptance rate, minimizing half total error rate, and minimizing discrimination loss.