  This paper describes a system by which Unmanned Aerial Vehicles (UAVs) can
gather high-quality face images that can be used in biometric identification
tasks. Success in face-based identification depends in large part on the image
quality, and a major factor is how frontal the view is. Face recognition
software pipelines can improve identification rates by synthesizing frontal
views from non-frontal views by a process call {\em frontalization}. Here we
exploit the high mobility of UAVs to actively gather frontal images using
components of a synthetic frontalization pipeline. We define a frontalization
error and show that it can be used to guide an UAVs to capture frontal views.
Further, we show that the resulting image stream improves matching quality of a
typical face recognition similarity metric. The system is implemented using an
off-the-shelf hardware and software components and can be easily transfered to
any ROS enabled UAVs.
