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In this paper, we describe a face video database, UTK-LRHM, acquired from long distances and with high magnifications. Both indoor and outdoor sequences are collected under uncontrolled surveillance conditions. To our knowledge, it is the first database to provide face images from long distances (indoor: 10-16m and outdoor: 50-300m). The corresponding system magnifications range from 3x to 20x for indoor and up to 284x for outdoor. This database has applications in experimentations with human identification and authentication in long range surveillance and wide area monitoring. Deteriorations unique to long range and high magnification face images are investigated in terms of face recognition rates based on the UTK-LRHM database. Magnification blur is shown to be a major degradation source, the effect of which is quantified using a novel blur assessment measure and alleviated via adaptive deblurring algorithms. A comprehensive processing algorithm, including frame selection, enhancement, and super-resolution is introduced for long range and high magnification face images with a large variety of resolutions. Experimental results using face images of the UTK-LRHM database demonstrate a significant improvement in recognition rates after assessment and enhancement of degradations.