Filtering Iris Match Scores Utilizing Segmentation Evaluation

The last result we present demonstrates one potential application of the technique. We use the overall segmentation result, output from the ensemble, to selectively filter match scores from the three different data sets. Due to the small number of incorrectly segmented images in the WVU and ICE data sets, we do not present results when using the Zuo’s segmenta-tion algorithm. Instead we focus on the Masek and IDO segmentasegmenta-tion. As mensegmenta-tioned in the

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Table 4.7: Annular Distance Ratio + Pupil Over/Under-Segmentation + Iris Edge: C-SVM Ensemble Misclassifications. A G = actual good segmentation, A F = actual failed segmentation, P G = predicted good segmentation, P F = predicted failed segmentation

introduction, iris segmentation is a main factor in determining an iris recognition system’s ability to successfully classify pairs of iris image as genuine or imposter. Along those lines, we would expect to see performance drop as the number of incorrectly segmented iris images increases. Figure 4.16 shows match score ROC curves based on segmentation results across the WVU, ICE and QFIRE data sets for both segmentation algorithms. In each Figure, a total of five curves are shown. The blue curves show the matching performance when all match scores are included and serves as a baseline. The highest performing solid (green) curves represent the matching performance from scores corresponding to pairs where both images were segmented correctly (ground truth). The highest performing dotted (green) curves represent the match scores of image pairs when the iris images were evaluated by the ensemble to have successfully segmented the iris image. Conversely, the lowest performing

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Figure 4.16: Iris recognition performance ROC’s based on segmentation result evaluations. (a) Masek-WVU, (b) IDO-WVU (c) Masek-ICE, (d) IDO-ICE, (e) Masek-QFIRE, (f) IDO-QFIRE

solid curves (red) represent the matching performance for pairs of images that failed seg-mentation (ground truth). Finally, the dotted red curves show the matching performance for pairs of images that were evaluated as having failed segmentation boundaries. These graphs are useful in that we confirm two concepts. One, we confirm the previously accepted premise that matching performance is significantly impacted by segmentation results. Per-haps the more useful conclusion is that the proposed ensemble accurately evaluates overall segmentation results as the matching performance of the filtered results corresponding to the evaluated data closely resembles the matching performance of the filtered results for the ground truth data.

4.6 Chapter Summary

We presented an approach to automatically measure the results of iris segmentation algo-rithms. Scores are provided for the two boundaries relevant to the task: pupil segmentation and iris segmentation. We evaluated the approach using three algorithms across three pub-licly available data sets. The results indicate the approach is capable of arriving at segmen-tation scores suitable for evaluating both the success and failure of pupil or iris segmensegmen-tation.

Additionally, we present a ensemble based machine learning approach to arrive at an over-all segmentation result which achieves an average classification accuracy of 92.52 % across all combinations of algorithms and data sets tested. We also presented one application of the proposed technique where the overall iris segmentation evaluation is used to filter iris recognition matching scores into correctly segmented and incorrectly segmented scoring bins.

Here we confirmed that iris match scores hailing from images that were evaluated to have

good segmentation scores perform more accurately than pairs which were evaluated to have failed segmentation. While this is one application of the technique, the technique should prove useful in many other arenas such as iris quality metrics involving local analysis, image reacquisition, and a means to signal the need for more intensive segmentation processing for segmentation rectification.

Segmentation Rectification

Recall that segmentation evaluation has many potential applications, not the least of which are related to image recapture or selectively filtering images that have been evaluated to be correctly segmented. The latter was demonstrated in Chapter 4. In this chapter we describe the more operationally relevant and certainly more attractive applications of segmentation evaluation. Notably, the scenarios involving the application of segmentation rectification.

5.1 Introduction

Segmentation rectification, simply put, is the process of re-segmenting the input iris image and serves as a potentially more attractive alternative to image recapture dependant upon the application. When segmentation fails, image recapture may not always be feasible for a given operational scenario. In particular, surveillance applications (e.g., iris at a distance) or portal scenarios which advocate high subject throughput cannot afford the opportunity for image recapture. As a result, segmentation rectification strategies must be explored. Here,

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we enumerate in the following list several segmentation rectification strategies explored in this chapter.

1. Black box rectification ∼ S1 - A simple yet potentially effective strategy may involve the application of a more computationally expensive segmentation algorithm, observed to provide better segmentation performance. However, such a strategy can be susceptible to making the same errors as the original and in time critical applica-tions, just not as feasible. Furthermore, most commercial algorithms are proprietary, and obtaining additional, reportedly better, segmentation algorithms may be cost pro-hibitive, ultimately limiting the applicability or utility of said strategy. Nevertheless, this option is still certainly worth exploring. Here, the initial base segmentation al-gorithms are treated as a black box, providing only the segmentation boundaries as an initial segmentation output. When the segmentation evaluation ensemble detects a failed segmentation subsequent processing attempts to rectify said segmentation. Note that only the overall classification output from the ensemble is utilized.

2. Rectification through operator conditioning ∼ S2 - The next strategy discussed in this chapter, S2, is fundamentally different from strategy S1. Here we assume that access to the internals of the initial base segmentation algorithm is available, specifi-cally the search operator space. Thus, the segmentation evaluation measures can be applied straightforwardly to the search operators of the algorithm in question. That is, the evaluation measures may be utilized to condition the search operator such that a correct boundary is more likely to be chosen from the operator search space. This is

advantageous in that segmentation completes in only a single pass. As a result, this strategy is computationally more efficient than strategies S1 and S3.

3. Black box rectification with Segmentation Evaluation ∼ S3 - This strategy is a combination of S1 and S2, with the exception that operator conditioning is only applied when the ensemble flags a failed segmentation. Further, we utilize the indi-vidual C-SVM models (as well as their features) to indicate which boundary requires rectification. Since additional a priori information, related to the segmentation bound-aries, is available through the individual evaluation measures, we can make a better judgement on how to rectify the incorrect segmentation. Given the proprietary nature of most vendor algorithms, it may not be possible to rectify segmentation boundaries with the same algorithm which provided the original segmentation. Like strategy S1, the initial base segmentation algorithms are treated as a black box, providing only the segmentation boundaries as an initial segmentation output. Thus, to rectify failed segmentation we build upon existing segmentation algorithms in the public domain, and modify them such that they take advantage of the information provided by the individual evaluation measures and the output of their respective models.

The aforementioned list of rectification strategies is by no means exhaustive but does serve as a starting point for exploring the potential of rectifying failed segmentation. Throughout the remainder of this chapter, we provide a look into the feasibility of segmentation rectifi-cation based on the aforementioned strategies. For the latter two strategies we make use of

the Masek and IDO segmentation algorithms combined with information generated by the segmentation evaluation block. We test the accuracy of each strategy using three different iris data sets. Additionally, we investigate the impact that each rectification strategy has on iris matching performance.