Unsupervised detection of non-iris occlusions

• Accurate iris defect detection.
• Unconstrained mobile devices high resolution color iris images.
• Multispectral Markovian spatial texture model.
• Ranked first from the 97+1 recent Noisy Iris Challenge Evaluation contest alternative methods.
• Promising performance on the very challenging, high resolution, and highly variable Mobile Iris Challenge Evaluation data.

This paper presents a fast precise unsupervised iris defects detection method based on the underlying multispectral spatial probabilistic iris textural model and adaptive thresholding applied to demanding high resolution mobile device measurements. The accurate detection of iris eyelids and reflections is the prerequisite for the accurate iris recognition, both in near-infrared or visible spectrum measurements. The model adaptively learns its parameters on the iris texture part and subsequently checks for iris reflections using the recursive prediction analysis. The method is developed for color eye images from unconstrained mobile devices but it was also successfully tested on the UBIRIS v2 eye database. Our method ranked first from the 97+1 recent Noisy Iris Challenge Evaluation contest alternative methods on this large color iris database using the exact contest data and methodology.

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