Standoff iris recognition using non-iterative polar based segmentation

Hamza, Rida; Whillock, Rand

doi:10.1117/12.777206

Cited by 1 publication

(1 citation statement)

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“…[7,8] Thus, an effective iris recognition method is one that should initially overcome the rigid constraints imposed during iris image acquisition, and offer both acceptable accuracy and fast processing speed. [9][10][11] Unconstrained iris recognition with less rigid image taking conditions can impose minimal to no constraints on the iris identification and verification process. To provide acceptable accuracy measures, it is critical for such iris recognition system to be complemented by a noise tolerant iris segmentation approach that to overcomes various noises introduced through image capture under different recording environments and scenarios.…”

Section: Introductionmentioning

confidence: 99%

A computational efficient iris extraction approach in unconstrained environments

Chen

Adjouadi

Barreto

et al. 2009

2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems

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This research introduces a noise-resistant and computational efficient segmentation approach towards less constrained iris recognition. The UBIRIS.v2 database which contains close-up eye images taken under visible light is used to test the proposed algorithm. The proposed segmentation approach is based on a modified and fast Hough transform augmented with a newly developed strategy to define iris boundaries with multi-arcs and multi-lines. This optimized iris segmentation approach achieves excellent results in both accuracy (2% error) and execution speed (≤0.5s / image) using a 2.4GHz Intel® Q6600 processor with 2GB of RAM. This 2% error is an Exclusive-OR function in term of disagreeing pixels between the correct iris considered by the NICE.I committee and the segmented results from the proposed approach. The segmentation performance was independently evaluated in the "Noisy Iris Challenge Evaluation", involving 97 participants worldwide, and ranking this research group in the top 6.

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