Oculométrie Numérique Economique : modèle d'apparence et apprentissage par variétés

Abstract : Gaze tracker offers a powerful tool for diverse study fields, in particular eye movement analysis. In this thesis, we present a new appearance-based real-time gaze tracking system with only a remote webcam and without infra-red illumination. Our proposed gaze tracking model has four components: eye localization, eye feature extraction, eye manifold learning and gaze estimation. Our research focuses on the development of methods on each component of the system. Firstly, we propose a hybrid method to localize in real time the eye region in the frames captured by the webcam. The eye can be detected by Active Shape Model and EyeMap in the first frame where eye occurs. Then the eye can be tracked through a stochastic method, particle filter. Secondly, we employ the Center-Symmetric Local Binary Patterns for the detected eye region, which has been divided into blocs, in order to get the eye features. Thirdly, we introduce manifold learning technique, such as Laplacian Eigen-maps, to learn different eye movements by a set of eye images collected. This unsupervised learning helps to construct an automatic and correct calibration phase. In the end, as for the gaze estimation, we propose two models: a semi-supervised Gaussian Process Regression prediction model to estimate the coordinates of eye direction; and a prediction model by spectral clustering to classify different eye movements. Our system with 5-points calibration can not only reduce the run-time cost, but also estimate the gaze accurately. Our experimental results show that our gaze tracking model has less constraints from the hardware settings and it can be applied efficiently in different real-time applications.
Document type :
Theses
Liste complète des métadonnées

https://tel.archives-ouvertes.fr/tel-02099612
Contributor : Abes Star <>
Submitted on : Monday, April 15, 2019 - 10:46:44 AM
Last modification on : Tuesday, April 16, 2019 - 11:16:06 PM

File

2015EPHE3020.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-02099612, version 1

Collections

Citation

Ke Liang. Oculométrie Numérique Economique : modèle d'apparence et apprentissage par variétés. Apprentissage [cs.LG]. École pratique des hautes études - EPHE PARIS, 2015. Français. ⟨NNT : 2015EPHE3020⟩. ⟨tel-02099612⟩

Share

Metrics

Record views

12

Files downloads

2