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Exploiting superior CNN-based iris segmentation for better recognition accuracy
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DOI：10.1016/j.patrec.2018.12.021.png)
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Abstract
En 中文
CNN-based iris segmentations have been proven to be superior to traditional iris segmentation techniques in terms of segmentation error metrics. To properly utilize them in a traditional biometric recognition systems requires a parameterization of the iris, based on the generated segmentation, to obtain the normalised iris texture typically used for feature extraction. This is an unsolved problem. We will introduce a method to parameterize CNN based segmentation, bridging the gap between CNN based segmentation and the rubbersheet-transform. The parameterization enables the CNN segmentation as full segmentation step in any regular iris biometric system, or alternatively the segmentation can be utilized as a noise mask for other segmentation methods. Both of these options will be evaluated. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
CNN
Iris segmentation
Iris biometrics
Parameterization of iris masks
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