Experiments with an Improved Iris Segmentation Algorithm
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
ICCI '06 Proceedings of the 2006 5th IEEE International Conference on Cognitive Informatics - Volume 02
Iris image segmentation and sub-optimal images
Image and Vision Computing
Noisy iris segmentation with boundary regularization and reflections removal
Image and Vision Computing
Neural-based iterative approach for iris detection in iris recognition systems
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Pattern Recognition
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The paper presents two different methods to deal with the problem of iris segmentation: an agent-based method capable to localize the center of the pupil and a method to process the iris boundaries by a multiple views approach. In the first method, an agent corresponds to the coordinates of a specific point of analysis in the input image. A population of agents is deployed in the input image, then, each agent collects local information concerning the intensity patterns visible in its region of interest. By iterations, an agent changes its position accordingly to the local properties, moving towards the estimation of the pupil center. If no available information is present in its region of interest, the agent will move itself along a random walk. After few iterations, the population tends to spread and then concentrate in the inner portion of the pupil. Once the center of the pupil has been located, the inner and outer iris boundaries are refined by an approach based on multiple views analysis. This method starts with a set of points that can be considered as an approximation of the pupil center. For each point, a detailed estimation of the iris boundaries is computed, and the final description of the iris boundaries is obtained by merging all the obtained descriptions. The two methods were tested using CASIA v.3 and UBIRIS v.2 images. Experiments show that the proposed approaches are feasible, also in eye images taken in noisy or non-ideal conditions, achieving a total error segmentation accuracy up to 97%.