Quality-based iris segmentation-level fusion

Wild, P., Hofbauer, H., Ferryman, J. and Uhl, A. (2016) Quality-based iris segmentation-level fusion. EURASIP Journal on Information Security, 2016. 25. ISSN 1687-417X doi: 10.1186/s13635-016-0048-x

Abstract/Summary

Iris localisation and segmentation are challenging and critical tasks in iris biometric recognition. Especially in non-cooperative and less ideal environments, their impact on overall system performance has been identified as a major issue. In order to avoid a propagation of system errors along the processing chain, this paper investigates iris fusion at segmentation-level prior to feature extraction and presents a framework for this task. A novel intelligent reference method for iris segmentation-level fusion is presented, which uses a learning-based approach predicting ground truth segmentation performance from quality indicators and model-based fusion to create combined boundaries. The new technique is analysed with regard to its capability to combine segmentation results (pupillary and limbic boundaries) of multiple segmentation algorithms. Results are validated on pairwise combinations of four open source iris segmentation algorithms with regard to the public CASIA and IITD iris databases illustrating the high versatility of the proposed method.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/68950
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Springer
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