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Some insights on iris localisation - getting a better accuracy.

asked 2015-07-27 03:33:38 -0500

updated 2020-09-17 05:54:56 -0500

I have been trying to port some python code on github for iris recognition to OpenCV for the past few days. I have been succesfully porting the code but kind of got stuck now.

If I look at the results of his test image he gets a pretty decent result image description

However, I get the following result [used the same input image!] image description

The code that I am using for this is

// -----------------------------------
// STEP 2: find the iris outer contour
// -----------------------------------
// Detect iris outer border
// Apply a canny edge filter to look for borders
// Then clean it a bit by adding a smoothing filter, reducing noise
Mat blacked_canny, preprocessed;
Canny(blacked_pupil, blacked_canny, 5, 70, 3);
GaussianBlur(blacked_canny, preprocessed, Size(7,7), 0, 0);

// Now run a set of HoughCircle detections with different parameters
// We increase the second accumulator value until a single circle is left and take that one for granted
int i = 80;
Vec3f found_circle;
while (i < 151){
   vector< Vec3f > storage;
   // If you use other data than the database provided, tweaking of these parameters will be neccesary
   HoughCircles(preprocessed, storage, CV_HOUGH_GRADIENT, 2, 100.0, 30, i, 100, 140);
   if(storage.size() == 1){
      found_circle = storage[0];
      break;
   }
   i++;
}
// Now draw the outer circle of the iris
// For that we need two 3 channel BGR images, else we cannot draw in color
Mat blacked_c(blacked_pupil.rows, blacked_pupil.cols, CV_8UC3);
Mat in[] = { blacked_pupil, blacked_pupil, blacked_pupil };
int from_to[] = { 0,0, 1,1, 2,2 };
mixChannels( in, 3, &blacked_c, 1, from_to, 3 );
circle(blacked_c, Point(found_circle[0], found_circle[1]), found_circle[2], Scalar(0,0,255), 1);
imshow("outer region iris", blacked_c); waitKey(0);

Getting the black pupil area was no problem and works flawlessly. I am guessing it goes wrong at detecting the edges uses the HoughTransform. I like the idea of looping over the accumulator value until a single circle remains, but it does not seem to be the same result.

Anyone has an idea on how I could improve the result?

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Comments

A quick update, found out which terrible hack he is doing, getting back to you guys with the answer in a second...

StevenPuttemans gravatar imageStevenPuttemans ( 2015-07-27 03:51:54 -0500 )edit

hmmm, sudden raise of interest in biometrics ? ;)

berak gravatar imageberak ( 2015-07-27 08:28:21 -0500 )edit
1

@berak, working on a PacktPub book ;) have been doing this at my masters year, but didnt got the time to dig deeper into it, now I got the opportunity to write a complete chapter about it ^_^

StevenPuttemans gravatar imageStevenPuttemans ( 2015-07-27 08:32:32 -0500 )edit
1

ahh, cool ! best of luck. ;)

berak gravatar imageberak ( 2015-07-27 08:38:55 -0500 )edit

Thank you! And the other chapter discusses the Viola Jones object detection interface in huge details! Once the book is released there will be new visualisation tools and tutorials coming ;)

StevenPuttemans gravatar imageStevenPuttemans ( 2015-07-27 08:40:43 -0500 )edit

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answered 2015-07-27 03:57:39 -0500

So what actually happens is the following

  • Once the pupil was segmented, the centroid was stored
  • Then a circle was looked for, which is sometimes out of line, but like 95% of the time the shape of the iris itself
  • He then aligns both centers, and draws the found hough circle on the centroid stored

This results in

image description

PROBLEM SOLVED! WHOPPA!

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Asked: 2015-07-27 03:33:38 -0500

Seen: 2,104 times

Last updated: Jul 27 '15