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Iris Recognition Based on SIFT Features
Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain. (ATVS/Biometric Recognition Group)ORCID iD: 0000-0002-1400-346X
Universidad Autonoma de Madrid, Spain.
Universidad Autonoma de Madrid, Spain.
Universidad Autonoma de Madrid, Spain.
2009 (English)In: 2009 First IEEE International Conference on Biometrics, Identity and Securit, 2009, 1-8 p.Conference paper (Refereed)
Abstract [en]

Biometric methods based on iris images are believed to allow very high accuracy, and there has been an explosion of interest in iris biometrics in recent years. In this paper, we use the Scale Invariant Feature Transformation (SIFT) for recognition using iris images. Contrarily to traditional iris recognition systems, the SIFT approach does not rely on the transformation of the iris pattern to polar coordinates or on highly accurate segmentation, allowing less constrained image acquisition conditions. We extract characteristic SIFT feature points in scale space and perform matching based on the texture information around the feature points using the SIFT operator. Experiments are done using the BioSec multimodal database, which includes 3,200 iris images from 200 individuals acquired in two different sessions. We contribute with the analysis of the influence of different SIFT parameters on the recognition performance. We also show the complementarity between the SIFT approach and a popular matching approach based on transformation to polar coordinates and Log-Gabor wavelets. The combination of the two approaches achieves significantly better performance than either of the individual schemes, with a performance improvement of 24% in the Equal Error Rate.

Place, publisher, year, edition, pages
2009. 1-8 p.
National Category
Signal Processing
URN: urn:nbn:se:hh:diva-21215DOI: 10.1109/BIDS.2009.5507529ScopusID: 2-s2.0-77954979896ISBN: 978-142445276-7OAI: diva2:589332
IEEE Proc. Intl. Conf. on Biometrics, Identity and Security, BIDS, Tampa, FL, Sept 22-23, 2009
Available from: 2013-01-17 Created: 2013-01-16 Last updated: 2015-09-29Bibliographically approved

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Alonso-Fernandez, Fernando
Signal Processing

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