Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/85821
Title: | Exploring deep learning image super-resolution for iris recognition |
Authors: | Ribeiro, Eduardo Uhl, Andreas Alonso-Fernandez, Fernando Farrugia, Reuben A. |
Keywords: | Deep learning (Machine learning) Optical data processing Pattern recognition Artificial intelligence High resolution imaging |
Issue Date: | 2017 |
Publisher: | IEEE |
Citation: | Ribeiro, E., Uhl, A., Alonso-Fernandez, F., & Farrugia, R. A. (2017, August). Exploring deep learning image super-resolution for iris recognition. 2017 25th European Signal Processing Conference (EUSIPCO). 2176-2180. |
Abstract: | In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) with the most possible lightweight structure to achieve fast speed, preserve local information and reduce artifacts at the same time. We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/85821 |
Appears in Collections: | Scholarly Works - FacICTCCE |
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File | Description | Size | Format | |
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Exploring_deep_learning_image_super-resolution_for_iris_recognition.pdf Restricted Access | 789.94 kB | Adobe PDF | View/Open Request a copy |
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