A Bayesian approach for modeling sensor influence on quality, liveness and match score values in fingerprint verification

Recently a number of studies in fingerprint verification have combined match scores with quality and liveness measures in order to thwart spoof attacks. However, these approaches do not explicitly account for the influence of the sensor on these variables. In this work, we propose a graphical model that accounts for the impact of the sensor on match scores, quality and liveness measures. The proposed graphical model is implemented using a Gaussian Mixture Model based Bayesian classifier. Effectiveness of the proposed model has been assessed on the LivDet11 fingerprint database using Biometrika and Italdata sensors.