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Mobile NIR iris recognition: identifying problems and solutions

Hofbauer, H., Jalilian, E., Sequeira, A., Ferryman, J. and Uhl, A. (2019) Mobile NIR iris recognition: identifying problems and solutions. In: Proceedings of the IEEE 9th International Conference on Biometrics: Theory, Applications, and Systems (BTAS2018), 22nd October 2018, Los Angeles, USA, pp. 1-9.

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The spread of biometric applications in mobile devices handled by untrained users opened the door to sources of noise in mobile iris recognition such as larger extent of rotation in the capture and more off-angle imagery not found so extensively in more constrained acquisition settings. As a result of the limitations of the methods in handling such large degrees of freedom there is often an increase in segmentation errors. In this work, a new near-infrared iris dataset captured with a mobile device is evaluated to analyse, in particular, the rotation observed in images and its impact on segmentation and biometric recognition accuracy. For this study a (manually annotated) ground truth segmentation was used which will be published in tandem with the paper. Similarly to most research challenges in biometrics and computer vision in general, deep learning techniques are proving to outperform classical methods in segmentation methods. The utilization of parameterized CNN-based iris segmentations in biometric recognition is a new but promising field. The results presented show how this CNN-based approach outperformed the segmentation traditional methods with respect to overall recognition accuracy for the dataset under investigation.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:81559

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