Abstract book (pdf) - ICPR 2010

scale analysis of iris images to enhance the performance. The experimental results presented in this paper are highly promising
and suggest the computationally attractive alternative for the online iris identification.
16:00-16:20, Paper WeCT6.2
Segmentation of Unideal Iris Images using Game Theory
Roy, Kaushik, Concordia Univ.
Bhattacharya, Prabir, Concordia Univ.
Suen, Ching Y.
Robust localization of inner/outer boundary from an iris image plays an important role in iris recognition. However, the
conventional iris/pupil localization methods using the region-based segmentation or the gradient-based boundary finding
are often hampered by non-linear deformations, pupil dilations, head rotations, motion blurs, reflections, non-uniform intensities,
low image contrast, camera angles and diffusions, and presence of eyelids and eyelashes. The novelty of this research
effort is that we apply a parallel game-theoretic decision making procedure by using the modified Chakra borty
and Duncan’s algorithm, which integrates the region-based segmentation and gradient-based boundary finding methods
and fuses the complementary strengths of each of these individual methods. This integrated scheme forms a unified approach,
which is robust to noise and poor localization.
16:20-16:40, Paper WeCT6.3
Iris-Biometric Hash Generation for Biometric Database Indexing
Rathgeb, Christian, Univ. of Salzburg
Uhl, Andreas, Univ. of Salzburg
Performing identification on large-scale biometric databases requires an exhaustive linear search. Since biometric data
does not have any natural sorting order, indexing databases, in order to minimize the response time of the system, represents
a great challenge. In this work we propose a biometric hash generation technique for the purpose of biometric database
indexing, applied to iris biometrics. Experimental results demonstrate that the presented approach highly accelerates biometric
identification.
16:40-17:00, Paper WeCT6.4
A Robust Iris Localization Method using an Active Contour Model and Hough Transform
Koh, Jaehan, SUNY Buffalo
Govindaraju, Venu, Univ. at Buffalo
Chaudhary, Vipin, SUNY Buffalo
Iris segmentation is one of the crucial steps in building an iris recognition system since it affects the accuracy of the iris
matching significantly. This segmentation should accurately extract the iris region despite the presence of noises such as
varying pupil sizes, shadows, specular reflections and highlights. Considering these obstacles, several attempts have been
made in robust iris localization and segmentation. In this paper, we propose a robust iris localization method that uses an
active contour model and a circular Hough transform. Experimental results on 100 images from CASIA iris image database
show that our method achieves 99% accuracy and is about 2.5 times faster than the Daugman’s in locating the pupillary
and the limbic boundaries.
17:00-17:20, Paper WeCT6.5
Isis: Iris Segmentation for Identification Systems
Nappi, Michele, Univ. of Salerno
Riccio, Daniel, Univ. of Salerno
De Marsico, Maria, Sapienza Univ. of Rome
Advances in processing procedures make the iris a realistic candidate to the role of biometry of the future. Precise detection
and segmentation for such biometry are a crucial ongoing research area. We propose an iris segmentation technique and
show that it is more reliable than existent ones.
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