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Eye regions localization

Eye regions localization. Bal á zs Harangi – University of Debrecen Ciprian Pop – Technical University of Cluj-Napoca L ás zl ó Kov á cs – University of Debrecen Norbert Hantos – University of Szeged. SSIP2009, Debrecen, Hungary. Outline. Overview

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Eye regions localization

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  1. Eye 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

  2. Outline • Overview • Block diagram • Skin segmentation • Morphological post-processing • Template matching • Corners detection • Pupil’s center and the iris localization • Experimental results • Conclusions • Future work

  3. Overview • The 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 thedevelopment of iris recognition systems is their incorporation into devices such as personal computers, mobile phones and embedded devices

  4. Block diagram

  5. Skin segmentation • We 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

  6. Morphological post-processing • For 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

  7. Template Matching • Template 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 techniques • We are using for two parameters: one for the size of those templates and one for the correlation threshold

  8. 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

  9. Corners detection • For 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:

  10. Pupil’s center and the iris localization • As a first step we cut out the founded eyes as regions of interest (ROIs) • We than transform them from the RGB to HSV • By thresholding the hue we obtain which are the segmented eyes • Than 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.

  11. Experimental results(1)

  12. Experimental results(2)

  13. Experimental results(3)

  14. Experimental results(4)

  15. Conclusions • Advantages • 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

  16. Future work • Use database of templates to find better match • Use better search algorithms to allow other parameters during registration • We 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

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