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Exploring Texture Transfer Learning via Convolutional Neural Networks for Iris Super Resolution

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2017

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Gesellschaft für Informatik, Bonn

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Increasingly, iris recognition towards more relaxed conditions has issued a new superresolution field direction. In this work we evaluate the use of deep learning and transfer learning for single image super resolution applied to iris recognition. For this purpose, we explore if the nature of the images as well as if the pattern from the iris can influence the CNN transfer learning and, consequently, the results in the recognition process. The good results obtained by the texture transfer learning using a deep architecture suggest that features learned by Convolutional Neural Networks used for image super-resolution can be highly relevant to increase iris recognition rate.

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Ribeiro,Eduardo; Uhl,Andreas (2017): Exploring Texture Transfer Learning via Convolutional Neural Networks for Iris Super Resolution. BIOSIG 2017. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-664-0. pp. 195-202. Further Conference Contributions. Darmstadt, Germany. 20.-22. September 2017

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