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Segmentation-level fusion for iris recognition

Wild, P., Hofbauer, H., Ferryman, J. and Uhl, A. (2015) Segmentation-level fusion for iris recognition. In: 14th International Conference of the Biometrics Special Interest Group (BIOSIG 2015), September 9-11, 2015, Darmstadt, Germany, pp. 1-6.

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Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb...

Abstract/Summary

This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:48462

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