Circular and are detected at several scales. MSER are

Circular Hough
Transform (CHT) was largely used in eyes detection, the circle is actually simpler to represent in
parameter space, compared to the line, since the parameters of the circle can be directly transfer to the
parameter space.

The CHT can be formulated as a convolution whose binary
mask coefficients are set on the circle boundary and are zero elsewhere.

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For the CHT calculation, a separate circle filter
can be used for each radius of circle to be detected. This forms the familiar
3-dimensional parameter space, usually associated with the CHT, where two
dimensions represent the position

of the circle centre (a, b), and the
third its radius r see (1).

 

 

 

III.3. Circle
Hough Transform Modifications

 

Modifications to the CHT was implemented in order to maximize algorithm convergence,
reduce complexity and even increase speed which is essential in real time
applications by.a single accumulator space for multiple radii, the use of edge
orientation, and implementation as convolution operators. Two patches resulting
from pre-processing step represent regions of interest we apply modified CHT on
each one to search circles representing irises (Left, Right). Centre of circle
obtained is taken as being centre eye. If it fails to find the circle the mean
of the patch become Centre eye.  

 

EVALUATION

 

Bio_ID database was chosen to test
our algorithm

 

 

 

Presence of Eyelids and eyelashes
make edge discontinuous and diverge CHT detector reason why we decide to
overcome this limitation by adding second detector which work parallelly to CHT
detector.   

MSER detection

 

 

Maximally Stable Extremal Region
(MSER) seems to be evident for eye localisation because of its nature, invariant
to continuous geometric transformations and affine intensity changes and are
detected at several scales. MSER are further considered as the fastest interest
point detection method This algorithm was proposed by Matas et al. 21. It
starts from intensity function of an image and end by Maximally Stable Extremal
Regions, where each region is defined by an extremal property of the intensity
function within the region to the values on its outer boundary. Elliptical
frames are attached to the MSERs by fitting ellipses to the regions. Those
regions descriptors are kept as features.

We apply MSER algorithm to regions
R1 and R2 previously found, in each region MSER gave us one unique region
representing eye region by ellipse and centre by ellipse centre following steps
shown in (Figure3).

The first step consiste of
performing a luminance thresholding on each Eye Region starting from black to
white, Regions are extracted from connected components called “Extremal
Regions”,  then compute local minimum of
the relative growth of its square that represent “Maximally Stable” regions
each Maximal Stable Extremal Region are approximated by ellipses. Eye Region is
resuting from tests on these Ellipse descriptors. We introduce algorithm to
choose best candidate represent true eye centre feature. Best Feauture is
choosen by (1) where n=number of ellipses found, Maxvariation is defined by
tests

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