Real-time gaze tracking with appearance-based models

Author:

Orozco Javier,Roca F. Xavier,Gonzàlez Jordi

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Reference20 articles.

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3. Clark, D.: Comparing huber’s m-estimator function with the mean square error in backpropagation networks when the training data is noisy. The Information Science Discussion Paper Series, 2000(19) (2000)

4. Cohen, I., Sebe, N., Chen, L., Garg, A., Huang, T.S.: Facial expression recognition from video sequences: temporal and static modeling. Comput. Vision Image Understand 91(1–2), 160–187 (2003)

5. Cootes, T.F., Taylor, C.J.: Statistical Models of Appearance for Computer Vision. University of Manchester (2004)

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1. Real-time eye tracking using representation learning and regression;Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD);2024-01-04

2. A Multi-Modal Gaze Tracking Algorithm;Journal of Circuits, Systems and Computers;2020-10-29

3. Eyelid and Pupil Landmark Detection and Blink Estimation Based on Deformable Shape Models for Near-Field Infrared Video;Frontiers in ICT;2019-10-14

4. On visual gaze tracking based on a single low cost camera;Signal Processing: Image Communication;2015-08

5. Statistically Learned Deformable Eye Models;Computer Vision - ECCV 2014 Workshops;2015

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