Paper Info
Title
Best regions for periocular recognition with NIR and visible images
Abstract
We evaluate the most useful regions for periocular recognition. For this purpose, we employ our periocular algorithm based on retinotopic sampling grids and Gabor analysis of the spectrum. We use both NIR and visible iris images. The best regions are selected via Sequential Forward Floating Selection (SFFS). The iris neighborhood (including sclera and eyelashes) is found as the best region with NIR data, while the surrounding skin texture (which is over-illuminated in NIR images) is the most discriminative region in visible range. To the best of our knowledge, only one work in the literature has evaluated the influence of different regions in the performance of periocular recognition algorithms. Our results are in the same line, despite the use of completely different matchers. We also evaluate an iris texture matcher, providing fusion results with our periocular system as well.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7026010
Image Processing
Keywords
Field
DocType
Gabor filters,image matching,image texture,iris recognition,Gabor analysis,NIR images,SFFS,discriminative region,iris texture matcher,periocular recognition,retinotopic sampling grids,sequential forward floating selection,skin texture,visible iris images,Biometrics,Gabor filters,eye,periocular
Signal processing,Computer vision,Pattern recognition,Computer science,Skin texture,Sclera,Artificial intelligence,Recognition algorithm,Biometrics,Discriminative model
Conference
ISSN
Citations 
PageRank 
1522-4880
2
0.36
References 
Authors
19
3
Name
Order
Citations
PageRank
Fernando Alonso-Fernandez153137.65
Josef Bigün2876194.07
Alonso-Fernandez, F.320.36