Wolfgang Fuhl
ABSTRACT
Undoubtedly, eye movements contain an immense amount of information, especially when looking to fast eye movements, namely time to the fixation, saccade, and micro-saccade events. While, modern cameras support recording of few thousand frames per second, to date, the majority of studies use eye trackers with the frame rates of about 120 Hz for head-mounted and 250 Hz for remote-based trackers. In this study, we aim to overcome the challenge of the pupil tracking algorithms to perform real time with high speed cameras for remote eye tracking applications. We propose an iterative pupil center detection algorithm formulated as an optimization problem. We evaluated our algorithm on more than 13,000 eye images, in which it outperforms earlier solutions both with regard to runtime and detection accuracy. Moreover, our system is capable of boosting its runtime in an unsupervised manner, thus we remove the need for manual annotation of pupil images.
- Mansour Asadifard and Jamshid Shanbezadeh. 2010. Automatic adaptive center of pupil detection using face detection and cdf analysis. In Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 1. 3.Google Scholar
- T. J. Atherton and D. J. Kerbyson. 1999. Size invariant circle detection. Image and Vision computing 17, 11 (1999), 795--803.Google Scholar
- C. Braunagel, E. Kasneci, W. Stolzmann, and W. Rosenstiel. 2015. Driver-Activity Recognition in the Context of Conditionally Autonomous Driving. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems. 1652--1657. Google ScholarDigital Library
- D. Droege and D. Paulus. 2010. Pupil center detection in low resolution images. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications. ACM, 169--172. Google ScholarDigital Library
- Wolfgang Fuhl, David Geisler, Thiago Santini, Wolfgang Rosenstiel, and Enkelejda Kasneci. 2016a. Evaluation of State-of-the-art Pupil Detection Algorithms on Remote Eye Images. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp '16). ACM, New York, NY, USA, 1716--1725. Google ScholarDigital Library
- W. Fuhl, T. Santini, and E. Kasneci. 2017. Fast and Robust Eyelid Outline and Aperture Detection in Real-World Scenarios. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). 1089--1097.Google Scholar
- W. Fuhl, T. C. Santini, T. Kübler, and E. Kasneci. 2016b. ElSe: Ellipse Selection for Robust Pupil Detection in Real-world Environments. In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA '16). ACM, New York, NY, USA, 123--130. Google ScholarDigital Library
- Wolfgang Fuhl, Marc Tonsen, Andreas Bulling, and Enkelejda Kasneci. 2016c. Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art. Machine Vision and Applications 27, 8 (2016), 1275--1288. Google ScholarDigital Library
- A. George and A. Routray. 2016. Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images. arXiv preprint arXiv:1605.05272 (2016).Google Scholar
- Kenneth Holmqvist, Marcus Nyström, Richard Andersson, Richard Dewhurst, Halszka Jarodzka, and Joost Van de Weijer. 2011. Eye tracking: A comprehensive guide to methods and measures. OUP Oxford.Google Scholar
- O. Jesorsky, K.J Kirchberg, and Robert W. F. 2001. Robust face detection using the hausdorff distance. In Audio-and video-based biometric person authentication. Springer, 90--95. Google ScholarDigital Library
- E. Kasneci. 2013. Towards the Automated Recognition of Assistance Need for Drivers with Impaired Visual Field. Ph.D. Dissertation. University of Tübingen, Wilhelmstr. 32, 72074 Tübingen. http://tobias-lib.uni-tuebingen.de/volltexte/2013/7033Google Scholar
- Moritz Kassner, William Patera, and Andreas Bulling. 2014. Pupil: An Open Source Platform for Pervasive Eye Tracking and Mobile Gaze-based Interaction. In Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '14 Adjunct). ACM, New York, NY, USA, 1151--1160. Google ScholarDigital Library
- X. Liu, F. Xu, and K. Fujimura. 2002. Real-time eye detection and tracking for driver observation under various light conditions. In Intelligent Vehicle Symposium, 2002. IEEE, Vol. 2. IEEE, 344--351.Google Scholar
- Gareth Loy and Alexander Zelinsky. 2003. Fast Radial Symmetry for Detecting Points of Interest. IEEE Trans. Pattern Anal. Mach. Intell. 25, 8 (Aug. 2003), 959--973. Google ScholarDigital Library
- S. Milborrow, J. Morkel, and F. Nicolls. 2010. The MUCT Landmarked Face Database. Pattern Recognition Association of South Africa (2010). http://www.milbo.org/muct.Google Scholar
- David A Robinson. 1963. A method of measuring eye movemnent using a scieral search coil in a magnetic field. Bio-medical Electronics, IEEE Transactions on 10, 4 (1963), 137--145.Google Scholar
- Thiago Santini, Wolfgang Fuhl, and Enkelejda Kasneci. 2018. PuRe: Robust pupil detection for real-time pervasive eye tracking. Computer Vision and Image Understanding (Feb 2018).Google Scholar
- Evangelos Skodras and Nikos Fakotakis. 2015. Precise Localization of Eye Centers in Low Resolution Color Images. Image Vision Comput. 36, C (April 2015), 51--60. Google ScholarDigital Library
- L. Świrski, A. Bulling, and N. Dodgson. 2012. Robust real-time pupil tracking in highly off-axis images. In Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA). ACM, 173--176. Google ScholarDigital Library
- Te Tan, Jan Philipp Hakenberg, and Cuntai Guan. 2013. Estimation of glance from EEG for cursor control. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE, 2919--2923.Google ScholarCross Ref
- F. Timm and E. Barth. 2011. Accurate Eye Centre Localisation by Means of Gradients. VISAPP 11 (2011), 125--130.Google Scholar
- A. Villanueva, V. Ponz, L. Sesma-Sanchez, M. Ariz, S. Porta, and R. Cabeza. 2013. Hybrid method based on topography for robust detection of iris center and eye corners. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 9, 4 (2013), 25. Google ScholarDigital Library
Index Terms
- BORE: boosted-oriented edge optimization for robust, real time remote pupil center detection
Recommendations
Evaluation of state-of-the-art pupil detection algorithms on remote eye images
UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: AdjunctEye movements are a powerful source of information as well as the most intuitive form of interaction. Although eye-tracking technology is still in its infancy, it offers the greatest potential for novel communication solutions and applications. Whereas ...
Measuring the task-evoked pupillary response with a remote eye tracker
ETRA '08: Proceedings of the 2008 symposium on Eye tracking research & applicationsThe pupil-measuring capability of video eye trackers can detect the task-evoked pupillary response: subtle changes in pupil size which indicate cognitive load. We performed several experiments to measure cognitive load using a remote video eye tracker, ...
MPB: Multi-Peak Binarization for Pupil Detection
Advanced Data Mining and ApplicationsAbstractAutomatic pupil detection is a fundamental part of eye-related tasks like eye tracking, gaze estimation and eye movement identification. Especially, in ophthalmology, to provide assistance and fulfil the demand of diagnosis and treatment, an ...
Comments