For digital imagery, face detection and identification are functions of great importance in wide-ranging applications, including full facial recognition systems. The development and evaluation of unique and existing face detection and face identification applications require a significant amount of data. Increased availability of such data volumes could benefit the formulation and advancement of many biometric algorithms. Here, the utility of using synthetically generated face data to evaluate facial biometry methodologies to a precision that would be unrealistic for a parametrically uncontrolled dataset, is demonstrated. Particular attention is given to similarity metrics, symmetry within and between recognition algorithms, discriminatory power and optimality of pan and/or tilt in reference images or libraries, susceptibilities to variations, identification confidence, meaningful identification mislabelings, sensitivity, specificity, and threshold values. The face identification results, in particular, could be generalized to address shortcomings in various applications and help to inform the design of future strategies.