Scenario-based score fusion for face recognition at a distance

Abstract

The effect of different acquisition distances on the performance of face verification is studied. In particular, we evaluate two standard approaches using popular features (DCT and PCA) and matchers (GMM and SVM) under variation in the acquisition distance, as well as their score-level combination. The DCT-GMM-based system is found to be more robust to acquisition distance degradation than the PCA-SVM-based system. We exploit this fact by introducing an adaptive score fusion scheme based on a novel automatic scenario estimation which is shown to improve our system in uncontrolled environments.