Eye Detection in Images
Introduction To Computational and biological
Vision
Lecturer : Ohad Ben Shahar
Written by : Itai Bechor
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Chapter Headings


Introduction
The Main algorithm:
 Detecting the face area
 Find a good candidates
 Find the most probability For Eyes in The
Image

Conclusions and Results
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Introduction
Detecting Eyes has many applications:
• For Face Recognition
• May Be Use By The Police
• In Security Services
• Future Use In Computers Security For Login
Propses
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Introduction
The Eye is Quite Unique Feature in the Face
 It might be easy to detect it more than other
elements in the face
 The Objective is To detect the Closest Area
To the eyes or the Eyes

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The Algorithm Diagram
Detect face
Find radius that suits eye
Detect the edge
Detect the eyes
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Images I work with
 Black and white images
 Head Images On a Plain Background
 Image resolution of 150x150 to 300x300
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Extraction of the face regions
Step 1
Input Image
M
N
Step 2
Canny Edge detector
Step 3
Calculate the left
and right bound
V(x)
x
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Face Region Extraction
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The Canny Edge Detector
 I used Gaussian 5x5
convolution To smooth the
image to clean the noise
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Canny Edge Detector

Compute gradient of g(m,n) using to get:

and

And finally by threshold m:
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Hough Circle Transformation

in my program : I Find The Circles In The Image From Radius 1 to width/2.
A circle in 2d is :

The accumulator Holding the Votes For each Radius.

Largest vote (a,b)
r
(Xi,Yi)
Edge point
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Hough Circle Transformation
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Hough Circle Transformation
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Selecting the Eyes
 Labeling Function That Find the best
Match Between Two Circles In The
Eyes
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Selecting the Eyes
Using the Following Methods:
1. Calculate the Distances between each two
circles .
2. The Slope Between The Two Circles.
3. The Radius similarity between two circles.
4. Large Number of circles in the same area
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Experimental Results
 Good Results:
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Experimental Results
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Experimental Results
 Bad Result: Hough Didn’t detect eye circles
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Experimental Results
 Bad Result: Label
Function Didn’t detect
eyes.
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Conclusion
The Algorithm need to be improved
In Order To Improve it :

1. Need To Use A Eyes Database
2. There is special cameras that can detect the
eye using an effect called The bright pupil
effect .
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