Accurate, robust, inexpensive gaze tracking in the car can help keep a driver\nsafe by facilitating the more effective study of how to improve (1) vehicle\ninterfaces and (2) the design of future Advanced Driver Assistance Systems. In\nthis paper, we estimate head pose and eye pose from monocular video using\nmethods developed extensively in prior work and ask two new interesting\nquestions. First, how much better can we classify driver gaze using head and\neye pose versus just using head pose? Second, are there individual-specific\ngaze strategies that strongly correlate with how much gaze classification\nimproves with the addition of eye pose information? We answer these questions\nby evaluating data drawn from an on-road study of 40 drivers. The main insight\nof the paper is conveyed through the analogy of an "owl" and "lizard" which\ndescribes the degree to which the eyes and the head move when shifting gaze.\nWhen the head moves a lot ("owl"), not much classification improvement is\nattained by estimating eye pose on top of head pose. On the other hand, when\nthe head stays still and only the eyes move ("lizard"), classification accuracy\nincreases significantly from adding in eye pose. We characterize how that\naccuracy varies between people, gaze strategies, and gaze regions.\n