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The effect of different acquisition distances on the performance of face verification is studied. In particular, the authors 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 PCASVM- based system. They exploit this fact by introducing an adaptive score fusion scheme based on a novel automatic scenario estimation which is shown to improve their system in uncontrolled environments.
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