Face detection is the first step in an automated system that identifies individuals based on their face's appearance. Detection is not trivial because the face is a non-rigid object that varies in position, rotation and size. The face's appearance can be altered by variations in expression, skin tone and lighting. This thesis evaluates popular detectors on imagery with known conditions. It shows that the Bayesian classifier is preferred under structured lighting conditions and the cascade classifier is preferred under unstructured lighting conditions. Finally, the lessons learned from detecting in unstructured light are applied to detect drivers as they pass a security checkpoint. A drive-by detection system in unique because the driver's side window and outdoor conditions pose possible problems. A multi-biometric detector that uses ears and profile faces to detect drivers is shown to work better than a simple, single-site detector.