Name
Abstract | ||
|---|---|---|
Eyeglasses change the appearance and visual perception of facial images. Moreover, under objective metrics, glasses generally deteriorate the sample quality of near-infrared ocular images and as a consequence can worsen the biometric performance of iris recognition systems. Automatic detection of glasses is therefore one of the prerequisites for a sufficient quality, interactive sample acquisition process in an automatic iris recognition system. In this paper, three approaches (i.e. a statistical method, a deep learning based method and an algorithmic method based on detection of edges and reflections) for automatic detection of glasses in near-infrared iris images are presented. Those approaches are evaluated using cross-validation on the CASIA-IrisV4-Thousand dataset, which contains 20000 images from 1000 subjects. Individually, they are capable of correctly classifying 95-98% of images, while a majority vote based fusion of the three approaches achieves a correct classification rate (CCR) of 99.54%. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICB2018.2018.00039 | 2018 International Conference on Biometrics (ICB) |
Keywords | Field | DocType |
Biometrics,Iris Recognition,Glasses Detection | Iris recognition,Computer vision,Pattern recognition,Computer science,Near-infrared spectroscopy,Artificial intelligence,Biometrics,Deep learning,Majority rule,Classification rate,Visual perception,Brightness | Conference |
ISSN | ISBN | Citations |
2376-4201 | 978-1-5386-4286-3 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pawel Drozdowski | 1 | 0 | 0.34 |
| Florian Struck | 2 | 3 | 0.78 |
| Christian Rathgeb | 3 | 551 | 55.72 |
| Christoph Busch | 4 | 268 | 50.22 |