Iris recognition using shape-guided approach and game theory |
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Authors: | Kaushik Roy Prabir Bhattacharya Ching Y Suen |
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Affiliation: | (1) Pattern Analysis and Machine Intelligence Research Group, University of Waterloo, Waterloo, ON, N2L 3G1, Canada;(2) Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221-0030, USA;(3) Centre for Pattern Recognition and Machine Intelligence (CENPARMI), Concordia University, Montreal, QC, H3G 1M8, Canada |
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Abstract: | Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled
environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different
noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast,
luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The novelty of this research effort
is that we propose to apply a variational model to localize the iris region belonging to given shape space using active contour
method, a geometric shape prior, and the Mumford–Shah functional. This variational model is robust against noise, poor localization
and weak iris/sclera boundaries. Furthermore, we apply the Modified Contribution-Selection Algorithm (MCSA) for iris feature
ranking based on the Multi-Perturbation Shapley Analysis (MSA), a framework which relies on cooperative game theory to estimate
the effectiveness of the features iteratively and select them accordingly, using either forward selection or backward elimination
approaches. The verification and identification performance of the proposed scheme is validated using the ICE 2005, the UBIRIS
Version 1, the CASIA Version 3 Interval, and WVU Nonideal datasets. |
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