Question

For those who find it too long, just read the bold lines.

My project of gaze estimation based screen cursor moving HCI is now dependent on one last thing – gaze estimation, for which i’m using eye corners as a reference stable point relative to which i will detect the movement of the pupil and calculate the gaze.

But i haven’t been able to stably detect eye corners from live webcam feed. I’ve been using cv.CornerHarris() and GFTT – cv.GoodFeaturesToTrack() functions for corner detection. I tried FAST demo (the executable from their website) directly on my eye images but that wasn’t good.

These are some results of my so far corner detections for images.

Using GFTT:

Using Harris:

what happens in video:

The green cirlces are the corners, the others (in pink, smaller circles) are the other corners

I used a certain heuristic – that the corners will be in the left or right extremeties and around the middle if thinking vertically.
I’ve done that because after taking many snapshots in many conditions, except for less than 5% of the images, rest are like these, and for them the above heuristics hold.

But these eye corner detections are for snapshots – not from the webcam feed.

When i use methodologies (harris and GFTT) for webcam feed, i just don’t get ’em.

My code for eye corner detection using cv.CornerHarris

Eye corners using GFTT

Now the parameters i use in both methods – they don’t show results for different lighting conditions and obviously. But in the same lighting condition as the one in which these snapshots were taken, i’m still not getting the result for the frames i queried from webcam video

These parameters from GFTT work good for average lighting conditions

cornerCount = 100
qualityLevel = 0.1
minDistance = 5

whereas these :

    cornerCount = 500
    qualityLevel = 0.005
    minDistance = 30

worked good for the static image displayed above

minDistance = 30 because obviously the corners would have atleast that much distance, again, something of a trend i saw from my snaps. But i lowered it for the webcam feed version of GFTT because then i wasn’t getting any corners at all.

Also, for the live feed version of GFTT, there’s a small change i had to accomodate:

cv.CreateImage((colorImage.width, colorImage.height), 8,1)

whereas for the still image version (code on pastebin) i used:

cv.CreateImage(cv.GetSize(grayImage), cv.IPL_DEPTH_32F, 1)

Pay attention to the depths.

Would that change any quality of detection??

The eye image i was passing the GFTT method didn’t have a depth of 32F so i had to change it and according the rest of the temporary images (eignenimg, tempimg ,etc)

Bottom line: I’ve to finish gaze estimation but without stable eye corner detection i can’t progress.. and i’ve to get on to blink detection and template matching based pupil tracking (or do you know better?). Put simply, i want to know if i’m making any rookie mistakes or not doing things which are stopping me from getting the near perfect eye corner detection in my webcam video stream, which i got in my snaps i posted here.

Anyways thanks for giving this a view. Any idea how i could perform eye corner detection for various lighting conditions would be very helpful

Okay, if you didn’t get what i’m doing in my code (how i’m getting the left and right corners), i’ll explain:

max_dist = 0
maxL = 20
maxR = 0

lc =0
rc =0

maxLP =(0,0)
maxRP =(0,0)

for point in cornerMem:
    center = int(point[0]), int(point[1])

    x = point[0]
    y = point[1]


    if ( x<colorImage.width/5 or x>((colorImage.width/4)*3) ) and (y>40 and y<70):
                      #cv.Circle(image,(x,y),2,cv.RGB(155, 0, 25))

                      if maxL > x:
                               maxL = x
                               maxLP = center


                      if maxR < x:
                               maxR = x
                               maxRP = center

                      dist = maxR-maxL

                      if max_dist<dist:
                           max_dist = maxR-maxL
                           lc = maxLP
                           rc = maxRP





    cv.Circle(colorImage, (center), 1, (200,100,255)) #for every corner

cv.Circle(colorImage,maxLP,3,cv.RGB(0, 255, 0)) # for left eye corner
cv.Circle(colorImage,maxRP,3,cv.RGB(0,255,0))   # for right eye corner

maxLP and maxRP will store the (x,y) for left and right corners of the eye respectively.
What i’m doing here is, taking a variable for left and right corner detection, maxL and maxR respectively, which will be compared
to the x-values of the corners detected. Now simply, for maxL, it has to be something more than 0; I assigned it 20 because if
the left corner is at (x,y) where x<20, then maxL will be = x, or if say, ie, the leftest corner’s X-ordinate is found this way. Similarly for rightest corner.

I tried for maxL = 50 too (but that would mean that the left corner is almost in the middle of the eye region) to get more candidates for the webcam feed – in which i’m not getting any corners at all

Also, max_dist stores the maximum distance between the so far seen X-ordinates, and thus gives a measure of which pair of corners
would be left and right eye corners – the one with the maximum distance = max_dist

Also, i’ve seen from my snapshots that the eye corners’ Y-ordinates fall in between 40-70 so i used that too to minimize
the candidate pool

Answer 1

i changed this

if ( x<colorImage.width/5 or x>((colorImage.width/4)*3) ) and (y>40 and y<70):

to this:

if ( x<(w/5) or x>((w/4)*3) ) and (y>int(h*0.45) and y<int(h*0.65)):

because earlier i was just manually looking at pixel values beyond which i my windows where corners could be found with the highest probability. But then afterwards i realised, lets make it general, so i made a horizontal window of 45 to 65 pc of the Y range, and 1/5th to 3/4ths for X range, because that’s the usual area within which the corners are.

I’m sorry guys for replying late, i was busy with the later part of the project – gaze estimation. And i’m gonna post a question about it, i’m stuck in it.

by the way, here are few pictures of eye corners and pupil detected in my eye:
(enlarged to 100×100)

Hope this will be useful for others beginning in this area.