TY - GEN
T1 - ElSe
T2 - 9th Biennial ACM Symposium on Eye Tracking Research and Applications, ETRA 2016
AU - Fuhl, Wolfgang
AU - Santini, Thiago C.
AU - Kübler, Thomas
AU - Kasneci, Enkelejda
N1 - Publisher Copyright:
© 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2016/3/14
Y1 - 2016/3/14
N2 - 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 Ltd. to Lab. Conditions. in Real-world Scenarios, Automat. Pupil Detection Has to Face Various Challenges, Such As Illumination Changes, Reflections , Make-up, Non-centered Eye Recording, and Physiological Eye Characteristics. We Propose ElSe, A Novel Algorith. Based on Ellipse Eval. of A Filtered Edge Image. We Aim at A Robust, Inexpensive Approach That Can Be Intgd. in Embedded Architectures, E.g., Driving. the Proposed Algorith. 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 Wk.. on Average, the Proposed Method Achieved A 14.53% Improvement on the Detection Rate Relative to the Best State-of-the-art Performer. Algorith. and Data Sets Are Available for Download: Ftp://[email protected].
AB - 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 Ltd. to Lab. Conditions. in Real-world Scenarios, Automat. Pupil Detection Has to Face Various Challenges, Such As Illumination Changes, Reflections , Make-up, Non-centered Eye Recording, and Physiological Eye Characteristics. We Propose ElSe, A Novel Algorith. Based on Ellipse Eval. of A Filtered Edge Image. We Aim at A Robust, Inexpensive Approach That Can Be Intgd. in Embedded Architectures, E.g., Driving. the Proposed Algorith. 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 Wk.. on Average, the Proposed Method Achieved A 14.53% Improvement on the Detection Rate Relative to the Best State-of-the-art Performer. Algorith. and Data Sets Are Available for Download: Ftp://[email protected].
KW - Eye tracking
KW - Pupil data set
KW - Pupil detection
UR - http://www.scopus.com/inward/record.url?scp=84975263325&partnerID=8YFLogxK
U2 - 10.1145/2857491.2857505
DO - 10.1145/2857491.2857505
M3 - Conference contribution
AN - SCOPUS:84975263325
T3 - Eye Tracking Research and Applications Symposium (ETRA)
SP - 123
EP - 130
BT - Proceedings - ETRA 2016
A2 - Spencer, Stephen N.
PB - Association for Computing Machinery
Y2 - 14 March 2016 through 17 March 2016
ER -