Regressor Based Estimation of the Eye Pupil Center

Abstract

The locations of the eye pupil centers are used in a wide range of computer vision applications. Although there are successful commercial eye gaze tracking systems, their practical employment is limited due to required specialized hardware and extra restrictions on the users. On the other hand, the precision and robustness of the off the shelf camera based systems are not at desirable levels. We propose a general purpose eye pupil center estimation method without any specialized hardware. The system trains a regressor using HoG features with the distance between the ground-truth pupil center and the center of the train patches. We found HoG features to be very useful to capture the unique gradient angle information around the eye pupils. The system uses a sliding window approach to produce a score image that contains the regressor estimated distances to the pupil center. The best center positions of two pupils among the candidate centers are selected from the produced score images. We evaluate our method on the challenging BioID and Columbia CAVE data sets. The results of the experiments are overall very promising and the system exceeds the precision of the similar state of the art methods. The performance of the proposed system is especially favorable on extreme eye gaze angles and head poses. The results of all test images are publicly available.