Eyes reflect our desires, when we are looking to something that mean
where our interest is. Many applications threat this subject. Applications on
HCI seem to be most interesting because of their daily use. On computers,
smartphones and other technologies, gaze can offer comfortable manner to
control like for guiding by gaze directions (robotic control) 2, eyes pretend
to become communication canal between interevent here, other applications like
security and control gain robustness by adding eyes detection and centre eyes
localisation, in commercial sectors when analyse direction of potential
customer can help to sale product or improve its efficiency. All these recent
applications prove eyes localisation importance focussing on centre eyes
localisation.

Different methods of eyes localisation are summarized in 3 they can
be divided into two different categories according to sources used. (i) IR illumination images: it is about cornea
reflexion on this type of image which is very helpful for centre eyes location.
(ii) light variations: the extreme variability of eyes appearance in real
environment make eyes centre very complex challenge to achieve, different
approaches are involved in this category, in this paper, we use a
feature-based approach for robust and accurate eye centre localisation with a
multi-stage scheme taking benefit of combining several methods, we make
contributions summarized in three points:

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(i) a fast
approach combining several methods

. (ii) We introduce
MSER regions for the first time in eyes localisation systems. (iii) We apply
simple technics like edge detection as a prepossessing step to reduce research
windows. Furthermore, we evaluate robustess by using the very challenging BioID
database. The obtained results are extensively compared with state of the art
methods for eye centre localisation.

Precise Eye centre Location
methods differs by computation, efficiency and more over precision degree.
Latest one is defined by a metric which is considered nowadays to be the major
evaluation metric used, it was introduced by 4. Three metrics
representing successively Eye region, Iris regions and Pupil (Centre eye)
represent how much location nears truthy real centre eye. Yang and al work 5
was one of the first impacting in Eye centre location using their special
features in the frequency domain by proposing a novel GaborEyebased method which
makes full use of the special gray distribution in the eye-and-brow region, and
self adaptively. Others use Gabor filters like Hamouz et al6identifing ten
feature points on the face through gabor filters, S.Kimm7 use multiscale
Gabor Feature vectors. 8 and 9 introduce isophote curvatures to infer the
centre of (semi)circular patterns and a novel centre voting mechanism, to perform very accurate eye centre
location and tracking.10 use isophote curvatures in fatigue detection
process, others 

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