Exploiting superior CNN-based iris segmentation for better recognition accuracy
| Autor: | Andreas Uhl, Ehsaneddin Jalilian, Heinz Hofbauer |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Biometrics
Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology 01 natural sciences Artificial Intelligence 0103 physical sciences Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing IRIS (biosensor) Segmentation Computer Vision and Pattern Recognition Artificial intelligence Noise (video) 010306 general physics business Software |
| Zdroj: | Pattern Recognition Letters |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2018.12.021 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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