1. 1 Eye Detection in Images Introduction To Computational and biological Vision Lecturer : Ohad Ben Shahar Written by : Itai Bechor

2. 2 Chapter Headings IIIIntroduction TTTThe Main algorithm: DDDDetecting the face area FFFFind a good candidates FFFFind the most probability For Eyes in The Image CCCConclusions and Results

3. 3 Introduction Detecting Eyes has many applications: For Face Recognition For Face Recognition May Be Use By The Police May Be Use By The Police In Security Services In Security Services Future Use In Computers Security For Login Propses Future Use In Computers Security For Login Propses

4. 4 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

5. 5 The Algorithm Diagram Detect face Detect the edge Find radius that suits eyeDetect the eyes

6. 6 Images I work with b Black and white images b Head Images On a Plain Background b Image resolution of 150x150 to 300x300

7. 7 Extraction of the face regions Step 1 Input Image Step 1 Input Image Step 2 Canny Edge detector Step 2 Canny Edge detector Step 3 Calculate the left and right bound Step 3 Calculate the left and right bound x V(x) N M

8. 8 Face Region Extraction

9. 9 The Canny Edge Detector  I used Gaussian 5x5 convolution To smooth the image to clean the noise

10. 10 The Gaussian Distribution Basic normal distribution skin Non-skin The mean vector The covariance matrix The probability density function

11. 11 Canny Edge Detector b Compute gradient of g(m,n) using to get: b and b And finally by threshold m:

12. 12 Hough Circle Transformation b b in my program : I Find The Circles In The Image From Radius 1 to width/2. b b A circle in 2d is : b b The accumulator Holding the Votes For each Radius. Edge point r (Xi,Yi) Largest vote (a,b)

13. 13 Hough Circle Transformation

14. 14 Hough Circle Transformation

15. 15 Selecting the Eyes b b Labeling Function That Find the best Match Between Two Circles In The Eyes

16. 16 Selecting the Eyes Using the Following Methods: 1. 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

17. 17 Experimental Results b Good Results:

18. 18 Experimental Results

19. 19 Experimental Results b Bad Result: Hough Didn’t detect eye circles

20. 20 Experimental Results b Bad Result: Label Function Didn’t detect eyes.

21. 21 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.