Name
Abstract | ||
|---|---|---|
Fast and robust pupil detection is an essential prerequisite for video-based eye-tracking in real-world settings. Several algorithms for image-based pupil detection have been proposed in the past, their applicability, however, is mostly limited to laboratory conditions. In real-world scenarios, automated pupil detection has to face various challenges, such as illumination changes, reflections (on glasses), make-up, non-centered eye recording, and physiological eye characteristics. We propose ElSe, a novel algorithm based on ellipse evaluation of a filtered edge image. We aim at a robust, inexpensive approach that can be integrated in embedded architectures, e.g., driving. The proposed algorithm was evaluated against four state-of-the-art methods on over 93,000 hand-labeled images from which 55,000 are new eye images contributed by this work. On average, the proposed method achieved a 14.53% improvement on the detection rate relative to the best state-of-the-art performer. Algorithm and data sets are available for download: ftp://[email protected] (password:eyedata).
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2857491.2857505 | ETRA |
Keywords | Field | DocType |
Pupil detection, Eye tracking, Pupil data set | Computer vision,File Transfer Protocol,Data set,Computer graphics (images),Computer science,Pupil,Eye tracking,Password,Artificial intelligence,Ellipse | Conference |
Volume | ISBN | Citations |
abs/1511.06575 | 978-1-4503-4125-7 | 27 |
PageRank | References | Authors |
0.96 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| wolfgang fuhl | 1 | 123 | 11.95 |
| Thiago C. Santini | 2 | 29 | 1.32 |
| Thomas C. Kübler | 3 | 124 | 12.57 |
| Enkelejda Kasneci | 4 | 202 | 33.86 |