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