PhD ThesisThe continued threat to security in our interconnected world today begs for urgent
solution. Iris biometric like many other biometric systems provides an alternative solution
to this lingering problem. Although, iris recognition have been extensively studied, it is
nevertheless, not a fully solved problem which is the factor inhibiting its implementation
in real world situations today. There exists three main problems facing the existing iris
recognition systems: 1) lack of robustness of the algorithm to handle non-ideal iris
images, 2) slow speed of the algorithm and 3) the applicability to the existing systems in
real world situation. In this thesis, six novel approaches were derived and implemented
to address these current limitation of existing iris recognition systems.
A novel fast and accurate segmentation approach based on the combination of graph-cut
optimization and active contour model is proposed to define the irregular boundaries of
the iris in a hierarchical 2-level approach. In the first hierarchy, the approximate boundary
of the pupil/iris is estimated using a method based on Hough’s transform for the pupil and
adapted starburst algorithm for the iris. Subsequently, in the second hierarchy, the final
irregular boundary of the pupil/iris is refined and segmented using graph-cut based active
contour (GCBAC) model proposed in this work. The segmentation is performed in two
levels, whereby the pupil is segmented first before the iris. In order to detect and eliminate
noise and reflection artefacts which might introduce errors to the algorithm, a preprocessing
technique based on adaptive weighted edge detection and high-pass filtering
is used to detect reflections on the high intensity areas of the image while exemplar based
image inpainting is used to eliminate the reflections. After the segmentation of the iris
boundaries, a post-processing operation based on combination of block classification
method and statistical prediction approach is used to detect any super-imposed occluding
eyelashes/eyeshadows. The normalization of the iris image is achieved though the rubber
sheet model.
In the second stage, an approach based on construction of complex wavelet filters and
rotation of the filters to the direction of the principal texture direction is used for the
extraction of important iris information while a modified particle swam optimization
(PSO) is used to select the most prominent iris features for iris encoding. Classification
of the iriscode is performed using adaptive support vector machines (ASVM).
Experimental results demonstrate that the proposed approach achieves accuracy of
98.99% and is computationally about 2 times faster than the best existing approach.Ebonyi State
University and Education Task Fund, Nigeri