Iris recognition based on SIFT features
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
IEEEFecha de edición
2009-12Cita
10.1109/BIDS.2009.5507529
IEEE International Conference on Biometrics, Identity and Security (BIdS). IEEE, 2009, 1-8.
ISSN
-ISBN
978-1-4244-5277-4 (online); 978-1-4244-5276-7 (print)DOI
10.1109/BIDS.2009.5507529Versión del editor
http://dx.doi.org/10.1109/BIDS.2009.5507529Materias
Error analysis; Feature extraction; Image matching; Image segmentation; Image texture; Iris recognition; Wavelet transforms; InformáticaNota
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. F. alonso-Fernández, P. Tomé-González, V. Ruiz-Albacete, J. Ortega-García, "Iris recognition based on SIFT features" in International Conference on Biometrics, Identity and Security (BIdS), Tampa, FL (USA), 2009, 1-8.Derechos
© 2009 IEEEResumen
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.
Lista de ficheros
Google Scholar:Ruiz-Albacete, Virginia
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Tomé, Pedro
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Ortega García, Javier
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Alonso-Fernández, Fernando
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