Iris recognition based on non-local comparisons

Authors:
henan Sun;Tieniu Tan;Yunhong Wang
Affiliations:
Center for Biometric Research and Testing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R China;Center for Biometric Research and Testing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R China;Center for Biometric Research and Testing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R China
Venue:
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Year:
2004

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Abstract

Iris recognition provides a reliable method for personal identification Inspired by recent achievements in the field of visual neuroscience, we encode the non-local image comparisons qualitatively for iris recognition In this scheme, each bit iris code corresponds to the sign of an inequality across several distant image regions Compared with local ordinal measures, the relation-ships of dissociated multi-pole are more informative and robust against intra-class variations Thus non-local ordinal measures are more suited for iris recognition In our early work, we have built a general framework “robust encoding of local ordinal measures” to unify several top iris recognition algorithms Therefore the results reported in this paper improve state-of-the-art iris recognition performance essentially as well as evolve the framework from pair-wise local ordinal relationship to non-local ordinal feature of multiple regions Our ideas are proved on CASIA iris image database.