Eye Detection Project Report. 7/19/2005

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Eye Detection
Project Report.


7/19/2005

Written By : Itai Bechor




Id: 034933564


Project Description
:


Eye Detection has many
Applications;

it can be used for Face Tracking, Recognition,

Personal Identication ,


The Main Goal was to find An Easy Way To
detect Eyes In Images , Using the
Following Simple Methods:

1.

Edge Detection

2.

Hough Circle

3.

Label Eye
s

Circles.


Explanation about the Methods I used:


1. Edge Detection


Canny Edge Detector

Edges characterize boundaries and are therefore a problem of fundame
ntal importance
in image processing. Edges in images are areas with strong intensity contrasts


a
jump in intensity from one pixel to the next. Edge detecting an image
significantly
reduces the amount of data and filters out useless information, while pre
serving
the important structural properties in an image.


The Canny edge detection algorithm is known to many as the optimal edge detector.
Canny's intentions were to enhance the many edge detectors already out at the time he
started his work. He was very
successful in achieving his goal and his ideas and
methods can be found in his paper, "
A Computational Approach to Edge Detection
".
In his paper, he followed a list of criteria to improve current methods of edge
detection. The first and most obvious is low

error rate. It is important that edges
occuring in images should not be missed and that there be NO responses to non
-
edges. The second criterion is that the edge points be well localized. In other words,
the distance between the edge pixels as found by th
e detector and the actual edge is to
be at a minimum. A third criterion is to have only one response to a single edge. This
was implemented because the first 2 were not substantial enough to completely
eliminate the possibility of multiple responses to an
edge.

Based on these criteria, the canny edge detector first smoothes the image to eliminate
and noise. It then finds the image gradient to highlight regions with high spatial
derivatives. The algorithm then tracks along these regions and suppresses any p
ixel
that is not at the maximum (nonmaximum suppression). The gradient array is now
further reduced by hysteresis. Hysteresis is used to track along the remaining pixels
that have not been suppressed. Hysteresis uses two thresholds and if the magnitude is
below the first threshold, it is set to zero (made a nonedge). If the magnitude is above
the high threshold, it is made an edge. And if the magnitude is between the 2
thresholds, then it is set to zero unless there is a path from this pixel to a pixel with

a
gradient above T2.


2. The Hough Transform


The Hough transform is a method that, in theory, can be used to find features of any
shape in an image. In practice it is only generally used for finding straight lines or
circles. The computational complexit
y of the method grows rapidly with more
complex shapes.

Assume we have some data points in an image which are perhaps the result of an edge
detection process, or boundary points of a binary blob. We wish to recognise the
points that form a straight line.

Consider a point (
x
i
,
y
i
) in the image. The general equation of a line is

y

=
ax

+
b
.

There are infinitely many lines that pass through this point, but they all satisfy the
condition

y
i

=
ax
i

+
b


for varying
a

and
b
.

We can rewrite this equation as

b

=
-
x
i
a

+
y
i
,

and plot the variation of
a

and
b
.

If we divide parameter space into a number of discrete accumulator cells we can
collect `votes' in
a b

space from each data point in
x y

space. Peaks in
a b

space will
mark the equations of lines of co
-
li
near points in
x y

space.

However, we have a problem with using
y

=
ax

+
b

to represent lines when the line is
vertical. In that case
an
d our parameter space is unbounded (we would need a
very large computer to store our parameter accumulator array!)

An alternative representation of a line is given by


where
r

is the distance of the line from the origin and
theta

is the angle between this
perpendicular and the x
-
axis. Our parameter space is now in
and
r
, where
and
r

is limited by the size of the image.

As before, peaks in the accumulator
array mark the equations of significant lines.

In theory any kind of curve can be detected if you can express it as a function of the
form


For example a circle can be represented as

(
x

-

a
)
2

+ (
y

-

b
)
2

-

r
2

= 0.

One then has to work in
n

dimensional parameter space (three dimensional space for a
circle).

This model has three parameters: two parameters for the centre of the circle and
one
parameter for the radius of the circle. If the gradient angle for the edges is available,
then this provides a constraint that reduces the number of degrees of freedom and
hence the required size of the parameter space. The direction of the vector from

the
centre of the circle to each edge point is determined by the gradient angle, leaving the
value of the radius as the only unknown parameter.

Thus, the parametric equations for a circle in polar coordinates are


x = a + r cos


a湤n


y = b + r sin

.

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a = x
-

r cos


a湤n


b = y
-

r sin

.

乯N⁧楶e渠瑨攠gra摩敮琠d湧汥l


at an edge point (x,y), we can compute cos


and sin

.
Note that these quantities may alr
eady be available as a by
-
product of edge detection.
We can eliminate the radius from the pair of equations above to yield


b = a tan

-

x⁴慮

+ y⸠

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Circle Fitting Algori
thm

1.

Quantise the parameter space for the parameters a and b.

2.

Zero the accumulator array M(a,b).

3.

Compute the gradient magnitude G(x,y) and angle

(x,y).

4.

For each edge point in G(x,y), increment all points in the accumulator array
M(a,b) along the line


b

= a tan

-

x⁴慮

+ y.

5.

Local maxima in the accumulator array correspond to centres of circles in the
image.


3.

Label Eyes Circles.


This Labeling System is designed for specific Portrait images, it work best with a
plain background.

This Method is tryi
ng To label The eyes Using a Simple Parameters and
Correlations between the Circles ,It Try To find The best Match , Two Circles
That Suits The Eyes .

I used the methods to give points to circles and to filter circles.

I'll explain the methods and the idea

behind each Method:

1. The Slope between the Circles.

Because I'm using a portrait images I'm Assuming the Head (and Though the
eyes) are having a slope of less than 45 degrees. I check For Each Circle If he got
a Friend Circle with that slope , if no I'
m

filtering him.

Lower Slope will Credit the Circles in More Points.

2. The Distance Between Two Circles.

I check That Distance is not Two Close Or not Too Far in Each 2 Circles, if I
found a isolated circle that have friend too far ( width /2 ) , because I
'm assuming
all the Face Is inside The image , I'll Filter him
, Good distance will give points to
the Circles
.

3. Number of Circles in the area of the Circle .

I notice in my testing with the hough circle that the area of the eyes gives a lot of
circles in

the same place or very close, So
I give Points To circles that are
congruent Depending on the number of circles.

4. The Radius compatibility Between the Circles

The Last Method is checking the Circle Radius ,if The Radius size is closer or
even compatible

You get more

points . the reason is obvious the eyes are in the
same size and probably the circles of the eyes are pretty much the same size.

The Code :

Since I used the Java Programming language I had a lot of work, because as you
know it's much easier
to do it on matlab.

I build An User Friendly Interface
,That allow you to load, and save image results,
and also in the options bar u can see all the 3 stages I talked about earlier in the
project description, Edge detection , hough circle and label eyes c
ircles.

So it's very simple.

Here is a screenshot:


The Classes are divided to :

EyeDetection
.java


Just The main.

Eye_d_gui.java


the Gui Of the Program .

Edge_detector.java


that I took the code from the internet ,The canny Edge
Detector.

Hough_Cir
cle.java


written by me ,make the hough circle transform and the
label eye .

ImageFilter
.java


filter the load and save images.

Picture.java


an
Enhancement

features To Image class

like grayscale
.









Results:


I have tested 20 Images that attached

to the Project file.

Note :

The Program is not complete ,it need a hard work and more Time To make
it work realy good , but I think it is in the right direction.


Face1.jpg , Resulotion : 235x180


Bad result , Reason : Hough Circle doesn't detect eyes

cir
cles
.


Face2.jpg , Resulotion : 235x180


Bad result , Reason : Hough Circle doesn't detect eyes

circles
.


Face3.jpg , Resulotion : 147x213


Bad result , Reason : Hough Circle doesn't detect eyes

circles
.


Face4.jpg , Resulotion : 200x178


Bad result , Reas
on : Hough Circle doesn't detect eyes

circles
.


Face5.jpg , Resulotion : 246x243


Bad result , Reason :
Hough Circle doesn't detect eyes circles


Face6.jpg , Resulotion : 175x231


Bad result , Reason :
Label eyes Circles doesn't detect eyes.


Face7.jpg , R
esulotion : 198x252


Bad result , Reason :
Label eyes Circles doesn't detect eyes


Face8.jpg , Resulotion : 194x201


Quite good

result , Reason : Hough Circle d
etect eyes , but not so good.


Face9.jpg , Resulotion : 260x284


Bad result , Reason : Hough Ci
rcle doesn't detect eyes.


Face10.jpg , Resulotion : 206x264


Good result .



Face11.jpg , Resulotion : 163x221


Good result .


Face12.jpg , Resulotion :
263
x2
94


Bad result , Reason : Hough Circle doesn't detect eyes.


Face13.jpg , Resulotion : 198x216


Good result .


Face14.jpg , Resulotion : 187x209


Good result .


Face15.jpg , Resulotion : 263x342


Bad result , Reason : Hough Circle doesn't detect eyes.


Face16.jpg , Resulotion : 161x176


Quite good result , Reason : Hough Circle detect eyes , b
ut not so good.

.

Face17.jpg , Resulotion : 224x274


Quite good

, Reason :
The Circle are very close to the eyes.


Face18.jpg , Resulotion : 2
11
x
2
2
5


Bad result , Reason : Hough Circle doesn't detect eyes

so good , and the label also
don't work very good
.


Face19.jpg , Resulotion : 263x342


Bad result , Reason : Hough Circle doesn't detect eyes.


Face20.jpg , Resulotion : 263x342


Bad result , Reason : Hough Circle doesn't detect eyes.


Conclusions :


From 20 images : 4 gives good results , 13 gives a bad
result , and 3 are quite
good .

That's mean that approximately the success is 30%.

Therefore The Program is not good enough to detect eyes, and need to be
improve :

The Following Methods need to be taken in order to make it work better:

1.

using An eye Templ
ates and check the correlation of The pixels to the
template. This could solve the Cases that Label Eyes Circles was unable To
detect the eyes.




Template example:





2 . As you all know there is an effect of red eyes in images , this effect can be used

in order to improve the detection of the circles of the eyes in images using the
hough circle method . But this improvement have to be Hardware improvement.