Authors:
Ke Liang
1
;
Youssef Chahir
1
;
Michèle Molina
2
;
Charles Tijus
1
and
François Jouen
1
Affiliations:
1
EPHE Paris, France
;
2
University of Caen, France
Keyword(s):
Appearance Eye Descriptor, Appearance-based Method, Manifold Learning, Spectral Clustering, Humancomputer Interaction.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
Abstract:
Eye-movements are increasingly employed to study usability issues in HCI (Human-Computer Interacetion)
contexts. In this paper we introduce our appearance-based eye control system which utilizes 5 specific eye
movements, such as closed-eye movement and eye movements with gaze fixation at the positions (up, down,
right, left) for HCI applications. In order to measure these eye movements, we employ a fast appeance-based
gaze tracking method with manifold learning technique. First we propose to concatenate local eye appearance
Center-Symmetric Local Binary Pattern(CS-LBP) descriptor for each subregion of eye image to form an eye
appearance feature vector. The calibration phase is then introduced to construct a trainning samples by spectral
clustering. After that, Laplacian Eigenmaps will be applied to the trainning set and unseen input together to
get the structure of eye manifolds. Finally we can infer the eye movement of the new input by its distances
with the clusters in the trainnin
g set. Experimental results demonstrate that our system with quick 4-points
calibration not only can reduce the run-time cost, but also provide another way to mesure eye movements
without mesuring gaze coordinates to a HCI application such as our eye control system.
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