چکیده (انگلیسی):

Iris segmentation is the most contested issue in the iris recognition system, since noise and poor image
quality can significantly affect accuracy of iris localization stage. Therefore, very careful attention has to be
paid for the segmentation process if only an accurate result is expected. This study presents a new method
for precise pupil detection capable of handling the unconstrained bad acquisition conditions especially those
related to low contrast or to the non-uniform brightness caused by the position of light sources, specular
reflection, eyelashes and eyelids. Contrast stretching (normalization) technique is used for handling the
variations in contrast and illumination in an iris image by stretching’ the range of intensity values. Next, the
local integration is applied on the enhanced image, this process will enhance the contrast level between the
existing white and black areas of the image; this will useful to compute the optimal threshold value required
to perform a successful image binarization for the purpose of isolation of the pupil region, the seed fill
algorithm is used as region growing method to segment the binary image and allocate the pupil as a circular
black segment with biggest area, the approximate pupil center is detected then for removing the specular
reflection, the pupil is filled with black color using a simple filling method. Finally a circle fitting algorithm
is used for precisely allocating the circular pupil region by the fact that richer iris textures are not closer to
the pupil boundary. A set of tests was conducted on 2,655 iris images which were downloaded from CASIA
V3.0-interval standard dataset; the test results indicated that the proposed method had subjectively 100%
accuracy rate with pupil localization, process satisfy the real time constraints even when dealing with
images have very different brightness or contrast conditions or they contain eyelashes artifacts.
Keywords: Image Enhancement, Histogram, Cumulative Histogram, Threshold, Pupil Detection, 4-
Neighbourhood Operator