Name
Abstract | ||
|---|---|---|
Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes. Specifically in the super-resolution field, Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution images. In this work we explore the viability of these approaches for iris Super-Resolution (SR) in an iris recognition environment. For this, we test different architectures with and without a so called image re-projection to reduce artifacts applying it to different iris databases to verify the viability of the different CNNs for iris super-resolution. Results show that CNNs and image re-projection can improve the results specially for the accuracy of recognition systems using a complete different training database performing the transfer learning successfully. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISBA.2019.8778581 | 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA) |
Keywords | Field | DocType |
iris recognition environment,iris super-resolution,transfer learning,low resolution images,iris databases,CNN,convolutional neural networks,image re-projection,deep learning | Iterative reconstruction,Signal processing,Iris recognition,Pattern recognition,Convolutional neural network,Computer science,Transfer of learning,Artificial intelligence,Deep learning,Discriminative model,Image resolution | Conference |
ISSN | ISBN | Citations |
2640-5555 | 978-1-7281-0533-8 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eduardo Ribeiro | 1 | 12 | 2.64 |
| Andreas Uhl | 2 | 1958 | 223.07 |
| Fernando Alonso-Fernandez | 3 | 531 | 37.65 |