Feature-domain super-resolution for IRIS recognition

Nguyen Thanh, Kien, Fookes, Clinton B., Sridharan, Sridha, & Denman, Simon (2011) Feature-domain super-resolution for IRIS recognition. In Proceedings of The 18th International Conference on Image Processing ICIP 2011, IEEE, Square Brussels Meeting Center, Brussels, pp. 3197-3200.

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Uncooperative iris identification systems at a distance suffer from poor resolution of the captured iris images, which significantly degrades iris recognition performance. Superresolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, all existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values. This paper considers transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. This is the first paper to investigate the possibility of feature domain super-resolution for iris recognition, and experiments confirm the validity of the proposed approach.

Impact and interest:

12 citations in Scopus
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166 since deposited on 06 Jun 2011
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ID Code: 41917
Item Type: Conference Paper
Refereed: Yes
Keywords: iris recognition, super-resolution, feature-domain super-resolution, feature-based super-resolution, iris recognition at a distance and on the move
DOI: 10.1109/ICIP.2011.6116348
ISBN: 978-1-4577-1304-0
ISSN: 1522-4880
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2011 IEEE
Deposited On: 06 Jun 2011 21:44
Last Modified: 24 Jun 2017 14:34

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