Paper Info
Title
Fake Iris Detection: A Comparison between Near-Infrared and Visible Images
Abstract
Fake iris detection has been studied so far using near-infrared sensors (NIR), which provide grey scale-images, i.e. With luminance information only. Here, we incorporate into the analysis images captured in visible range, with color information, and perform comparative experiments between the two types of data. We employ Gray-Level Cocurrence textural features and SVM classifiers. These features analyze various image properties related with contrast, pixel regularity, and pixel co-occurrence statistics. We select the best features with the Sequential Forward Floating Selection (SFFS) algorithm. We also study the effect of extracting features from selected (eye or periocular) regions only. Our experiments are done with fake samples obtained from printed images, which are then presented to the same sensor than the real ones. Results show that fake images captured in NIR range are easier to detect than visible images (even if we down sample NIR images to equate the average size of the iris region between the two databases). We also observe that the best performance with both sensors can be obtained with features extracted from the whole image, showing that not only the eye region, but also the surrounding periocular texture is relevant for fake iris detection. An additional source of improvement with the visible sensor also comes from the use of the three RGB channels, in comparison with the luminance image only. A further analysis also reveals that some features are best suited to one particular sensor than the others.
Year
DOI
Venue
2014
10.1109/SITIS.2014.104
SITIS
Keywords
Field
DocType
biometrics, attacks, fake iris, near-infrared iris, visible iris, glcm features,iris,databases,feature extraction,signal processing,image sensors,iris recognition
Iris recognition,Computer vision,Image sensor,Pattern recognition,Computer science,Support vector machine,Feature extraction,RGB color model,Artificial intelligence,Pixel,Biometrics,Luminance
Conference
Citations 
PageRank 
References 
0
0.34
18
Authors
3
Name
Order
Citations
PageRank
Fernando Alonso-Fernandez153137.65
Josef Bigün2876194.07
Alonso-Fernandez, F.300.34