IDO Segmentation

Next, we look at the pupil and iris scoring with our implementation of the integro-differential operator algorithm and the WVU data set. Figure4.11 shows the distributions of scores for images that have both good segmentation and failed segmentation. Figure 4.11(a) depicts the average annular distance ratio scoring results with the red histogram representing the distribution for images that failed segmentation and the green histogram corresponding to images that were correctly segmented. As illustrated by the plot, the distributions completely

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Masek − QFIRE −Segmentation Metrics ROC

Average Annular Distance Ratios Pupil Over−Segmentation Pupil Under−Segmentation Average Iris Edge Scores

Figure 4.10: ROC performance for the segmen-tation metrics generated from the Masek seg-mentation boundaries on the QFIRE data set.

Segmentation Metrics Mean Std distribu-tion statistics for Masek segmentadistribu-tion on the QFIRE data set.

overlap with the correctly segmented images having a mean score of 0.92 and the images that failed segmentation having a mean score of 0.90. In contrast, the same features for the Masek algorithm were well separated. Figure 4.11(b) presents the pupil over-segmentation scores which have much better separation between the distributions than the average an-nular histograms. The distributions for both failed and correct segmentation have a mean of 0.64 and 0.94, respectively. On the other hand, the pupil under-segmentation scores shown in plot4.11(c)have more overlap than the over-segmentation distributions. Here the scores corresponding to images with correctly segmented regions have a mean of 0.97 while the failed images scored 0.75 on average. The iris edge score distributions are provided in plot 4.11(d). Notably, the distributions are fairly well separated with a mean of 0.93 for correct iris segmentation and 0.58 for failed iris segmentation. This is corroborated by plot 4.11(e) which illustrates the ROC performance of all metric scores for the IDO algorithm on the WVU data set. More specifically, the EER for the iris edge scores, the black line

plot, is 6.87%. In contrast, the EERs for the remaining metrics are inferior to the iris edge metric. That is, the EERs for annular distance ratio, pupil over-segmentation, and pupil under-segmentation are 41.62%, 14.55%, and 25.50% respectively.

Figure4.12shows the distributions of scores for images that have both good segmentation and failed segmentation for the ICE data set when processed with the IDO segmentation algorithm. Figure 4.12(a) depicts the average annular distance ratio scoring results with the red histogram representing the distribution for images that failed segmentation and the green histogram corresponding to images that were correctly segmented. As illustrated by the plot, the distributions completely overlap with the correctly segmented images hav-ing a mean score of 0.91 and the images that failed segmentation havhav-ing a mean score of 0.88. This overlap was also observed for scores generated from the WVU data set. Fig-ure 4.12(b) presents the pupil over-segmentation scores which have much better separation between the distributions than the average annular histograms. The distributions for both failed and correct segmentation have a mean of 0.67 and 0.93, respectively. On the other hand, the pupil under-segmentation scores shown in plot 4.12(c) have more overlap than the over-segmentation distributions. Here the scores corresponding to images with correctly segmented regions have a mean of 0.97 while the failed images scored 0.83 on average. The iris edge score distributions are provided in plot 4.12(d). The distributions are fairly well separated with a mean of 0.95 for correct iris segmentation and 0.61 for failed iris segmen-tation. This trend was also observed for scores generated from the WVU data set. This is further corroborated by plot 4.12(e) which illustrates the ROC performance of all metric scores for the IDO algorithm on the ICE data set. More specifically, the EER for the iris edge scores, black line plot, is 3.51%. Likewise, the EER for the pupil over-segmentation scores is

(a) IDO-WVU: Average Annular Distance Ratios.

(b) IDO-WVU: Pupil Over-segmentation.

(c) IDO-WVU: Pupil Under-segmentation. (d) IDO-WVU: Average Iris Edge Scores.

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False Accept Rate

Genuine Accept Rate

IDO − WVU −Segmentation Metrics ROC

Average Annular Distance Ratios Pupil Over−Segmentation Pupil Under−Segmentation Average Iris Edge Scores

(e) IDO-WVU: Metric ROC.

Figure 4.11: Segmentation score distributions for IDO segmentation on ICE data (a) Average annular distance ratio distributions (b) Pupil-Over distributions (c) Pupil-under distributions (d) Average iris edge score distributions (e) Segmentation metric ROC.

also fairly reasonable at 4.00%. Similar to performance of the WVU data set, the remaining EERs of the other measures are inferior to the pupil over-segmentation and iris edge mea-sures. Specifically, the EERs for annular distance ratio and pupil under-segmentation are 36.28% and 31.05% respectively.

In Table 4.3 we provide summary statistics for both correct and failed IDO segmenta-tion when processing the subset of imagery from the Q-FIRE data set. The more notable observation in comparison to the statistics provided for the Masek algorithm, is that we less separation between the correct and failed distributions for the annular distance ratio scores. The mean of correct segmentation for annular distance ratios are .9076 while for failed segmentation the means shift to .8907. This observation holds across all data sets for this measure when evaluating segmentation from the IDO algorithm. The cause likely results from our implementation of the algorithm. After finding the pupil boundary, the iris search is constrained to evaluate locations within the pupil region near the pupil center, thus limiting the potential for gross inaccuracies. On the other hand, we observe slightly more separation for the iris edge information measure. Specifically, a mean of .8879 for correct segmentation is attained while for failed segmentation the mean is .5222.