Eye Feature Detection Towards Automatic Strabismus Screening

Transcriptie

1 Eye Feature Detection Towards Automatic Strabismus Screening Ken Allen, Khanh Nguyen Gettysburg College

2 What is strabismus? Eye defect that causes eyes to look in two different directions If left untreated, can cause blindness in one eye

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4 How is it detected? Corneal light reflex test (Hirschberg test) Uses positions of corneal reflections in each Displacement of reflection could signal strabismus

5 Goal of the project To investigate a software solution for strabismus screening intended to be used in professional photography studios Detect all eye features necessary for screening

6 Detection Process bounds of the face bounds of the eye region center of each pupil centers of a matched pair of corneal reflections Corners of the eyes

7 Face detection - goals Provide a focus-of of-attention mechanism for eye detection Shrink search region Speed up searching

8 Face detection Jones-Viola face detector provided by OpenCV library Both fast and accurate Misses closely cropped faces

9 Face detection results

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12 Face detection results

13 Eye region detection - goals Further refine focus-of of-attention mechanism Find general location of eyes

14 Eye region detection - method Thresholding

15 Eye region detection method Candidates Selection

16 Eye region detection - results

17 Iris detection - goals Locate the center of each iris Needed as a point of reference for highlight positions

18 Iris detection - method Hough Transformation to find the iris center candidates

19 Iris Detection method My diagram

20 Iris detection - observations - There are cases that it fails (put pix, and explain)

21 Highlight detection - goals Locate all specular light reflections Find best matched pair (same size and shape) Specular reflection

22 Highlight detection - method Create thresholded mask Find brightest pixel (in HSB colorspace) Grow region on mask to find extents Discard if region is too large to be a highlight Mark position on mask Repeat until all sufficiently bright pixels have been marked

23 Highlight detection - method

24 Highlight detection - method Pick one highlight in one eye Compare size and proportions of its bounding box with those of each highlight in opposing eye If both size and shape are the same, stop Otherwise pick another highlight and try again If no two highlights are exactly alike pick a pair that s s of the same shape

25 Highlight detection - results

26 Eye corner cetection - goals Locate the left and right corners of each eye Needed as reference points for highlight position Needed to determine the angle of the eyes

27 Eye corner detection - method Find primary contours with canny edge detector Build set of gradient points Find eye white region

28 Corner detection - results

29 Eye corner detection observations Most common false positive is the detection of the eyelid fold Biggest problem is lack of robustness across varying lighting Studio pictures are expected to be consistent Quite accurate when used on test images

30 Future Work Integrate all steps into one program Perform Hirschberg test Possibly replace or supplement corner and iris detection with new contour extraction algorithm

31 Acknowledgement Funded by Gettysburg College