Eye finding is the first step toward building a machine that can recognize social cues, like eye contact and gaze direction, in a natural context. In this paper, we present a real-time implementation of an eye finding algorithm for a foveated active vision system. The system uses a motion-based prefilter to identify potential face locations. These locations are analyzed for faces with a template-based algorithm developed by Sinha (1996). Detected faces are tracked in real time, and the active vision system saccades to the face using a learned sensorimotor mapping. Once gaze has been centered on the face, a high-resolution image of the eye can be captured from the foveal camera using a self-calibrated peripheral-to-foveal mapping. We also present a performance analysis of Sinha’s ratio template algorithm on a standard set of static face images. Although this algorithm performs relatively poorly on static images, this result is a poor indicator of real-time performance of the behaving system. We find that our system finds eyes in 94% of a set of behavioral trials. We suggest that alternate means of evaluating behavioral systems are necessary.