Abstract/Summary

Despite numerous biometric challenges promoting iris and ocular recognition from less controlled images, iris localization and segmentation are still challenging topics. Examples include the Multiple Biometrics Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS), Noisy Iris Challenge Evaluation (NICE) and Iris Challenge Evaluation (ICE). For controlled environments and as long as iris segmentation algorithms can be tuned for the employed set, the segmentation problem can be managed. However, independent studies have shown, that algorithms may fail considerably if preconditions are not met. For datasets like FOCS segmentation performance ranging from 51% accuracy (using an integro-differential operator), to 90% (for active contours) is reported. Violations of preconditions or assumptions of controlled conditions such as particular hardware challenge the generalisation of successful segmentation techniques. Especially non-circularity of boundaries, non-frontal acquisition, or blurred images can impose difficulties. On the other hand, very flexible parameterless solutions allowing high degrees of freedom (e.g. off-axis acquisition) are sensitive to noise or reflections. They sometimes deliver inferior results to classical techniques for images where certain preconditions hold. Examples of challenging iris images include weak boundaries or reflections in visible-range iris images, making it harder for iris local- ization and segmentation algorithms to properly segment acquired images. Further undesirable conditions are on-the-move motion blur, out-of-focus images, narrowed eyes to a slit, or images with weak contrast. Besides accuracy, also speed and usability play important factors for efficient iris segmentation. For quick rejection of frames in video streams containing no iris image or unlikely leading to a successful verification, quality measures as predictors of recognition or segmentation accuracy can be used. This is useful to save precious processing time, as processing errors at early stages usually also result in classification errors. This chapter reviews recent approaches of iris segmentation techniques and discusses approaches towards robust segmentation, highlighting the following topics: • Segmentation performance assessment: Performance assessments are usually done with regards to recognition accuracy (thus evaluating an entire processing chain rather than individual algorithms). However, systematic seg- mentation errors can have a minor impact on performance if the same algorithm is used to segment gallery and probe images. This scenario is not realistic in cross-sensor applications. However, ground-truth only evaluations are restrictive, as they do not account for the tolerance of small segmentation in- accuracies by the employed feature extraction and comparison algorithm (e.g. via pooling operations). Different forms of segmentation accuracy evaluation are presented. There are separate segmentation-only challenges (e.g. NICE) and also ground-truth sets available, to which this chapter con- tributes a manual segmentation of the multispectral iris database UTIRIS. • Near infrared (NIR) vs. visible range (VIS) iris segmentation and the tuning problem: Algorithms developed for specific image datasets or character- istics are not agnostic towards the type of imagery used. NIR images, for example, exhibit a pronounced inner pupillary boundary, while VIS images tend to have better outer limbic contrast. This is usually exploited by seg- mentation algorithms to achieve better accuracy on particular datasets. While more generic techniques offering the same processing chain for VIS and NIR images have been suggested, tuning can have a strong impact on perfor- mance. This chapter presents modifications to the open source USIT - Univer- sity of Salzburg Iris Toolkit for better performance, if prior knowledge on iris data characteristics (NIR vs. VIS, iris-to-pupil ratio, size of iris, etc.) is available. Source code of the modifications is released. • Iris fusion at segmentation-level: Instead of developing completely new seg- mentation algorithms, the idea behind segmentation-level fusion is to make efficient use of different existing optimised approaches estimating their ac- curacy. The best individual segmentation technique can be used as the final segmentation result. Techniques can even be combined at a lower level leading to new segmentation results. For example, over- and under-segmentation methods can be fused to balance errors towards more robust iris segmentation. A learning-based approach predicting ground-truth segmentation performance is compared to non-quality based combinations in a multispectral setup assessing the impact of NIR and VIS spectras on performance. New techniques are analysed with regards to a challenging multispectral NIR and VIS iris database involving the same subjects. This allows a direct comparison of robustness of segmentation results (localisation of pupillary and limbic boundaries) across the two spectra and illustrates the high versatility of the proposed methods.