| Summary: | 碩士 === 國立暨南國際大學 === 資訊工程學系 === 99 === Nowadays, automatic iris recognition systems can achieve excellent performance when
the pupil sizes of both the enrolled and the test iris images are approximately the same. How-
ever, whenthepupilsizechangesdramaticallyduetoeitherilluminationvariationoremotion
stimuli, the equal error rate (EER) will increase because of strong nonlinear iris deformation.
In this thesis, we focus on the normalization method for compensating the nonlinear iris de-
formation effect. Two nonlinear normalization approaches, i.e., the information equalization
(IE) method and the dynamic programming (DP) method, are proposed in this study. We also
implement Daugman’s rubber sheet model and Yuan’s nonlinear normalization model to be
compared with the two proposed methods. Furthermore, a special iris image database called
IrisDeform database is constructed with 228 classes of iris patterns from 114 subjects. Each
class in the IrisDeform database contains 160 iris patterns acquired at two different sessions
with 16 different illumination intensity levels and five iris images for each intensity level.
The IrisDeform database is useful for researchers working on the nonlinear normalization
problem of iris patterns. Two experiments are conducted to test the nonlinear normalization
algorithms. In the first experiment, we enroll the iris images by turns and use the remaining
images to method and Yuan’s nonlinear normalization method are 3.21% and 3.51%, respec-
tively, whereas the EERs of our IE and DP methods are 4.25% and 2.26%, respectively.
The second experiment aims to test the performance of different normalization methods at
the extreme cases. The enrolled and the test iris images are acquired at the darkest and the
brightest illumination intensity, respectively. The experimental results show that the EERs
of both the methods of Daugman and Yuan degraded to 7.21% and 8.05%, respectively, due
to strong iris deformation. Conversely, the EER of the DP method only increases to 6.93%.
The two experiments show that the proposed DP method is more tolerable to large nonlinear
deformations of iris patterns.
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