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Elastic strips normalisation model for higher iris recognition performance

Elastic strips normalisation model for higher iris recognition performance

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Iris recognition is among the best biometric systems. Characterised by the iris's uniqueness, universality, distinctiveness, permanence and collectability, the iris recognition system achieves high performance and real time response. In this study, the authors propose an improved iris normalisation model applied after a precise iris segmentation process. The normalisation model defines a new reference space for iris features. It normalises the iris using radial strips with a shape that changes between the pupil's boundary and the circular approximation of the iris's outer boundary. Moreover, the effect of the centres of the normalisation strips is evaluated by assessing the recognition performance when comparing three different centres configurations. The approach is tested on 2491 images from the CASIA V3 database. The system's performance is measured at the matching stage. Higher decidability and recognition accuracy at equal error rate is obtained. Detection error tradeoff curves are estimated by using the proposed model and compared with Daugman's reference system for assessing performance improvement.

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