Name
Title | ||
|---|---|---|
Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images |
Abstract | ||
|---|---|---|
Periocular refers to the facial region in the eye vicinity. It can be easily obtained with existing face and iris setups, and it appears in iris images, so its fusion with the iris texture has a potential to improve the overall recognition. It is also suggested that iris is more suited to near-infrared (NIR) illumination, whereas the periocular modality is best for visible (VW) illumination. Here, we evaluate three periocular and three iris matchers based on different features. As experimental data, we use five databases, three acquired with a close-up NIR camera, and two in VW light with a webcam and a digital camera. We observe that the iris matchers perform better than the periocular matchers with NIR data, and the opposite with VW data. However, in both cases, their fusion can provide additional performance improvements. This is specially relevant with VW data, where the iris matchers perform significantly worse (due to low resolution), but they are still able to complement the periocular modality. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IWBF.2015.7110234 | Biometrics and Forensics |
Keywords | Field | DocType |
image fusion,image matching,image texture,iris recognition,nir camera,nir illumination,webcam,digital camera,eye vicinity,iris image fusion,iris matcher,iris texture,near-infrared and visible images,near-infrared illumination,periocular matcher,periocular modality,visible illumination,iris,biometrics,fusion.,near-infrareddata,periocular,visible data,feature extraction,fusion,databases,signal processing,lighting,face | Computer vision,Iris recognition,Signal processing,Computer science,Near-infrared spectroscopy,Facial region,Fusion,Feature extraction,Artificial intelligence,Discrete cosine transforms | Conference |
ISSN | Citations | PageRank |
2381-6120 | 2 | 0.36 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alonso-Fernandez, F. | 1 | 2 | 0.36 |
| Anna Mikaelyan | 2 | 15 | 2.38 |
| Josef Bigün | 3 | 876 | 194.07 |
| Fernando Alonso-Fernandez | 4 | 531 | 37.65 |