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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Iris Recognition in Image Domain: Quality-Metric Based Comparators
verfasst von : Heinz Hofbauer, Christian Rathgeb, Andreas Uhl, Peter Wild
Erschienen in: Advances in Visual Computing
Verlag: Springer Berlin Heidelberg
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Traditional iris recognition is based on computing efficiently coded representations of discriminative features of the human iris and employing Hamming Distance (HD) as fast and simple metric for biometric comparison in feature space. However, the International Organization for Standardization (ISO) specifies iris biometric data to be recorded and stored in (raw) image form (ISO/IEC FDIS 19794-6), rather than in extracted templates (e.g. iris-codes) achieving more interoperability as well as vendor neutrality. In this paper we propose the application of quality-metric based comparators operating directly on iris textures, i.e. without transformation into feature space. For this task, the Structural Similarity Index measure (SSIM), Local Edge Gradients metric (LEG), Natural Image Contour Evaluation (NICE), Edge Similarity Score (ESS) and Peak Signal to Noise ratio (PSNR) is evaluated. Obtained results on the CASIA-v3 iris database confirm the applicability of this type of iris comparison technique.