27 citations · 50 across the 5 of their papers we have counts for
7 papers
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion
Fernando Alonso-Fernandez, Reuben A. Farrugia, Josef Bigun
Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a…
Face2Text revisited: Improved data set and baseline results
Marc Tanti, Shaun Abdilla, Adrian Muscat +3
Current image description generation models do not transfer well to the task of describing human faces. To encourage the development of more human-focused descriptions, we develope…
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning
F. Alonso-Fernandez, R. A. Farrugia, J. Bigun +2
The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution…
Improving Super-Resolution Performance using Meta-Attention Layers
Matthew Aquilina, Christian Galea, John Abela +2
Convolutional Neural Networks (CNNs) have achieved impressive results across many super-resolution (SR) and image restoration tasks. While many such networks can upscale low-resolu…
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields
Reuben A. Farrugia, C. Guillemot
Plenoptic cameras offer a cost effective solution to capture light fields by multiplexing multiple views on a single image sensor. However, the high angular resolution is achieved…
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Albert Gatt, Marc Tanti, Adrian Muscat +6
The past few years have witnessed renewed interest in NLP tasks at the interface between vision and language. One intensively-studied problem is that of automatically generating te…