In this chapter quality factors for the iris biometric were identified and their individual influ-ence on performance was studied for the Log-Gabor based encoding technique. We concluded that for all quality factors identified, they negatively influence recognition performance with the main degradation observed in the genuine scores as the degree of degradation increases.
Estimation procedures for defocus, motion blur, occlusion, specular reflection, light-ing, pixel counts were then carried out. Estimated quality scores are then fused by use of Dempster-Shafer theory in order to attain a combined metric for image quality. We tested the proposed quality algorithm on CASIA 3.0, WVU, and ICE 1.0 data sets. We noticed that CASIA 3.0 contains higher quality data than both ICE 1.0 and WVU. We empirically observed that our quality metric can predict performance for all three data sets, with im-provements in both EER and d′ as quality increases. This result indicates that our fused iris quality measure is suitable as an informal measure of dependability of the matching decision.
As such, the measure can be included in multi-modal fusion algorithms which utilize quality measures from each modality. Furthermore, we compared our quality metric to global iris quality metrics and concluded that although they are capable of predicting performance,
local metrics provide better prediction despite their obvious limitation of requiring segmen-tation. Our metric also has the advantage of providing descriptive feedback about the quality of the image (i.e. we can describe how the image is degraded and to what extent). This is useful when deciding another capture is required and can provide the operator with feedback regarding how the settings should be adjusted to capture a better quality image.
The main limitation of our approach is the requirement of segmentation. Failed lo-calization/segmentation will result in inaccurate quality scores. However, this would have negative consequence only if the matching algorithm applied to the same iris image performs segmentation successfully. Therefore, as long as the segmentation algorithm used for quality evaluation is as sophisticated as the one used in quality evaluation, it is unlikely that quality scores will be misleading [128]. Nevertheless, the need to deploy segmentation within the quality assessment algorithm makes this approach unsuitable for real-time applications in which a quality factor would be used for the selection of the “best” frame from a sequence (e.g., streaming video).
Aside from perfecting the estimation techniques for the described quality factors, experi-menting with the new quality scores that incorporate correlation may prove useful. Further, the proposed framework is open for the inclusion of new iris quality factors which will un-doubtedly emerge through further research [45] or through further relaxation of acquisition constraints (e.g. distance, motion, non-uniform lighting) [84]. To support such future devel-opment, we will maintain and expand our data sets, keep the data as well as the described algorithms publicly available and, therefore, encourage repeatability of our experimental results.
Segmentation Evaluation
4.1 Introduction
The performance of iris recognition systems is driven in part by application scenario require-ments. Standoff distance, subject cooperation, underlying optics, and illumination are just a few examples of factors associated with these requirements. These factors subsequently dictate the nature of images an iris recognition system will deal with. At the image level, iris segmentation is arguably one of the most important factors driving recognition performance.
That is, if the iris regions are successfully localized for pairs of images to be matched, the cor-rect classification will be almost always be made. Iris image segmentation typically consists of two problems. First, one must define the boundary between the pupil, the black region in the center of the eye and the iris, the textured region surrounding the pupil as shown by the inner green ring in Figure 4.1(a). Second, one must define the boundary between the iris and the sclera, or the lighter region surrounding the iris as shown by the outer blue ring in Figure4.1(a). Many methods exist for detecting these boundaries, a survey of segmentation
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algorithms is provided in Chapter5.3.
Whether due to limitations of algorithms or poor image quality, failed segmentation
(a) Correctly segmented image. (b) Failed pupil segmentation.
(c) Failed iris segmentation. (d) Failed pupil and iris segmentation
Figure 4.1: Four types of segmentation results (a) Correctly segmented image (b) Failed pupil segmentation (c) Failed iris segmentation (d) Failed pupil and iris segmentation.
often accounts for misclassification errors in iris recognition systems. As a result, the ability to automatically evaluate whether the segmentation block of an iris recognition system has
succeeded or failed is of paramount importance when attempting to predict the outcome of matching. Whether a binary success / failure flag or measures with higher granularity, currently existing algorithms do not explicitly evaluate the segmentation result. As a result, without human inspection, the success of segmentation blocks is largely unknown in most iris recognition systems. Having a tool which provides such information is useful in an op-erational sense in that it can serve as an indicator to reacquire a better image if feasible.
Otherwise, when reacquisition is not an option, such a measure could serve to flag entrance into a computationally more expensive automatic segmentation block (.e.g., segmentation rectification).
In this chapter, we provide a look into the feasibility of automatically evaluating the seg-mentation block of iris segseg-mentation algorithms. Our segseg-mentation evaluation methodology is a combination of metrics that utilize geometric, statistical intensity, and edge information.
Besides looking at the precision of the pupil and iris segmentation boundaries independently, we also provide the ability to arrive at a global binary segmentation evaluation result by way of a machine learning approach. We test the accuracy of the approach on three databases using three different segmentation algorithms. Additionally, to demonstrate one application of the tool, we investigate the effect that the varying success of segmentation has on iris matching performance.