GP/GPU Class final project to implement a GPU based eye iris segmentation to achieve improvement over an optimised equivalent CPU implementation.

1. CUDA based Iris
Detection based on
Hough Transform
G P - G P U C o u r s e P r o j e c t
J u s t i n a s M i š e i k i s

2. Goal
Eye detection in a color or grayscale image
Detect boundaries of iris and pupil for the
segmentation
Make the detection real-time

3. CUDA parts
Hough transform for circles
Peak detection
Image adjustment
Gamma correction
CPU parts
Canny edge detection - OpenCV
Histogram calculation for image adjustment

4. Optimisation
Limit possible radius, thus limit search space
Couple of voting locations served by one block
Automatic adjustment of thread and block
numbers to keep load higher - more efficient
When pupil is found, iris search space only around
the same centre
Avoiding host-device and device-host memory
copying
Using shared memory

5. Results - Images

6. Results - Timing
Time measurements of the whole process including
image pre-processing
Image size C++ CPU CUDA Speed up
1024x768 32.490 sec 3.947 sec 8.23x
800x600 17.161 sec 2.054 sec 8.35x
640x480 8.701 sec 1.120 sec 7.76x
500x375 4.202 sec 0.589 sec 7.13x
350x263 1.461 sec 0.293 sec 4.99x
250x188 0.625 sec 0.175 sec 3.57x
MATLAB with 640x480: over 32 sec

7. Results - Timing
Iris detection CPU vs CUDA
40.000
30.000
Time, sec
20.000
10.000
0
250x188 350x263 500x375 640x480 800x600 1024x768
Image size
CPU CUDA

8. Hardware Used
HP All-in-One 200-5300ch
Processor: Intel Core i3-550 (3.2GHz), 4MB
RAM: 2x 2048MB DDR3, 1333MHz
GPU: nVidia GeForce G210 with 512 MB (low-end)

9. Comments and
Future Improvements
Algorithm should work faster on a better GPU
Main bottleneck - AtomicAdd function
Get rid of any CPU based pre-processing
Make the whole process purely on GPU, no
memory copying involved except for the result
Automatic parameter adjustment to ensure good
results on variety of pictures

10. Dependencies
CUDA
Atomic functions
OpenCV

11. Thank You!
Any questions?