Computer Laboratory

Past projects


Robust, accurate, real-time pupil tracking is a key component for online gaze estimation. On head-mounted eye trackers, existing algorithms that rely on circular pupils or contiguous pupil regions fail to detect or accurately track the pupil. This is because the pupil ellipse is often highly eccentric and partially occluded by eyelashes. We present a novel, real-time dark-pupil tracking algorithm that is robust under such conditions. Our approach uses a Haar-like feature detector to roughly estimate the pupil location, performs a k-means segmentation on the surrounding region to refine the pupil centre, and fits an ellipse to the pupil using a novel image-aware Random Sample Concensus (RANSAC) ellipse fitting. We compare our approach against existing real-time pupil tracking implementations, using a set of manually labelled infra-red dark-pupil eye images. We show that our technique has a higher pupil detection rate and greater pupil tracking accuracy


  • Paper (PDF, 1.41 MB)
  • Source (C++, requires TBB, OpenCV and Boost)
  • Datasets (4 datasets, 3760 frames, 600 labelled)


    author    = {Lech \'Swirski and Andreas Bulling and Neil A. Dodgson},
    title     = {Robust real-time pupil tracking in highly off-axis images},
    booktitle = {Proceedings of ETRA},
    month     = mar,
    year      = {2012},
    location  = {Santa Barbara, CA, USA},
    url       = {},