Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iris Recognition with Low Template Size
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Iris Individuality: A Partial Iris Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Circuits and Systems for Video Technology
An efficient, two-stage iris recognition system
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
A contrario detection of false matches in iris recognition
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
On combining selective best bits of iris-codes
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
GESLIC: genetic and evolutionary-based short-length iris codes
Proceedings of the 49th Annual Southeast Regional Conference
A comparison of genetic feature selection and weighting techniques for multi-biometric recognition
Proceedings of the 49th Annual Southeast Regional Conference
Iris recognition based on robust iris segmentation and image enhancement
International Journal of Biometrics
Genetic and evolutionary methods for biometric feature reduction
International Journal of Biometrics
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The texture in a human iris has been shown to have good individual distinctiveness and thus is suitable for use in reliable identification. A conventional iris recognition system unwraps the iris image and generates a binary feature vector by quantizing the response of selected filters applied to the rows of this image. Typically there are 360 angular sectors, 64 radial rings, and 2 filter responses. This produces a full-length iris code (FLIC) of about 5760 bytes. In contrast, this paper seeks to shrink the representation by finding those regions of the iris that contain the most descriptive potential. We show through experiments that the regions close to the pupil and sclera contribute least to discrimination, and that there is a high correlation between adjacent radial rings. Using these observations we produce a short-length iris code (SLIC) of only 450 bytes. The SLIC is an order of magnitude smaller the FLIC and yet has comparable performance as shown by results on the MMU2 database. The smaller sized representation has the advantage of being easier to store as a barcode, and also reduces the matching time per pair.