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Eye regions localizationEye regions localization
Balázs Harangi – University of Debrecen Ciprian Pop – Technical University of Cluj-Napoca László Kovács – University of Debrecen Norbert Hantos – University of Szeged
SSIP2009, Debrecen, Hungary
OutlineOutlineOverviewBlock diagramSkin segmentationMorphological post-processingTemplate matchingCorners detectionPupil’s center and the iris
localizationExperimental resultsConclusionsFuture work
OverviewOverviewThe eye regions detection problem
has been studied extensively and it is of an increasing importance nowdays
The most important fields in what this kind of recognition are used are ◦ reliable biometric identification of people ◦ emotions recognition algorithms
One of the future challenges in the development of iris recognition systems is their incorporation into devices such as personal computers, mobile phones and embedded devices
Block diagramBlock diagram
Skin segmentationSkin segmentationWe start from a still colored image, and for it
we apply the RGB to YCbCr and RGB to HSV transformations between color spaces
For the color components the following formulas are used:
Original image
Skin segmentation result
Morphological post-Morphological post-processingprocessingFor filling the holes in the segmented image
we apply an erosion followed by a dilatation(opening) and than with a filling function in Matlab we fill the holes
Skin segmentation result
Face pixels
Template MatchingTemplate MatchingTemplate matching is used for the eye
localization and it is done by correlation For finding the most likely positions for the
eyes we use image registration techniquesWe are using for two parameters: one for
the size of those templates and one for the correlation threshold
Template matching(2)Template matching(2)The templates that we used are: Using this templates and the parameters
computed something like this is obtained:
The detected eyes based on
the correlation image are:
The template correlations
Corners detectionCorners detectionFor the corners detection we use
the templates corners coordinates and then we scaled them along with the eye templates when doing the eye matching
The result obtained is:
Pupil’s center and the iris Pupil’s center and the iris localization localizationAs a first step we cut out the founded eyes
as regions of interest (ROIs) We than transform them from the RGB to
HSVBy thresholding the hue we obtain
which are the segmented eyesThan we compute the center of the pupil by
computing the center of the white area, and from there we calculate the fit sized circle until we find
lighter pixels that are surely not
part of the iris.
Experimental results(1)Experimental results(1)
Experimental results(2)Experimental results(2)
Experimental results(3)Experimental results(3)
Experimental results(4)Experimental results(4)
ConclusionsConclusionsAdvantages
◦ Finding the corners by image registration is a easier
method
◦ Speed an results are good in case of suitable image
registration
◦ Easier algorithm comparing to others in literature
◦ It can be easily improved in time
Disadvantages
◦ The eye can’t always be registered because of the
parameter space
◦ The eye registration could fail if the eyes are very
different from the template
◦ Is not that fast as we wished it to be
Future workFuture workUse database of templates to find better matchUse better search algorithms to allow other
parameters during registrationWe can use some well known corners detection
algorithms like Harris or Susan for increasing it’s accuracy
For the pupil and iris localization we can use some better threshold algorithms, or fuzzy segmentation
Instead of the circles we can use ellipses to delineat the iris or we can use active contours