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Generating statistically significant datasets for face matching system evaluation is a laborious and expensive process. Capturing variables such as atmospheric turbulence and other weather conditions especially with respect to face recognition at a distance exacerbate the problem further. It is even more difficult to work on system issues for long-range systems that impact the collection phase such as automated control loops for gain, focus or zoom, as they directly impact the collected data. And since system performance is confounded with variations in subject selection, pose, lighting, expression, etc., formal evaluation of second order effects are difficult without extremely large collections. This paper describes a taxonomy of face-models for controlled experimentation that overcome these challenges. We show that a gap has existed in experimental design and how a range of model-based approaches can partially …