Are Iris Crypts Useful in Identity Recognition?

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1 Are Iris Crypts Useful in Identity Recognition? Feng Shen Patrick J. Flynn Dept. of Computer Science and Engineering, University of Notre Dame 254 Fitzpatrick Hall, Notre Dame, IN Abstract We conducted an experiment in which participants were asked to annotate the crypts they see in iris images. The results were used to assess the utility of crypts in identity recognition for forensic applications. Although the inter-participant annotation consistency may be limited by crypts noticeability and the range of genuine similarity scores obtained by the adopted matcher is correlated to the number of crypt pixels in the given image, the intra-iris crypt perception is sufficiently consistent for nearly 95% of query images to be correctly identified. The performance is expected to be better for the professional examiners with comprehensive training. 1. Introduction The iris is claimed to be one of the best performing biometric modes. It rarely suffers damage and changes very slowly, if at all, during adulthood. The iris recognition systems developed by Daugman [1] are reported to find no false matches in about 20 billion cross-comparisons [2] and can perform about 100,000 full comparisons between different irises per second [1]. Despite its advantages in speed and accuracy over other biometric modes, the iris has not been examined in the domain of forensics. One important reason is that the feature representation in traditional iris techniques is not easily interpreted by humans. As a result, it is difficult to add manual inspection to verify the system output, which is essential to many law enforcement applications. Some iris techniques have been proposed to exploit features that are better associated with the human vision. The method in [6] uses an adjacent pair of local minimum and maximum of 1D dyadic wavelet transform outputs to locate the appearance and disappearance of dark irregular blocks on each row of the normalized iris. Yang et al. [11] suggests using multi-channel 2D Gabor filters to detect key points that represent local texture information most effectively. De Mira et al. [7] use morphological operators to highlight ridge-like patterns and match irises by the extracted nodes, end-points and branches. Sunder et al. [10] employ SIFT to characterize the macro-features on the iris and use them for template retrieval. Unfortunately, none of the foregoing methods conclusively correlate the extracted feature with any specific types of visible features. An iris recognition technique that parallels fingerprint techniques has been proposed in [8] and [9]. It uses crypts as the minutiae of iris, and measures the similarity between two irises by the shapes and locations of the extracted crypts and the number of common crypts identified between two irises. The advantage of this technique is that it makes it much more convenient for forensic examiners to supervise the identification process because the representative features of irises and the identification results are directly viewable to human eyes. However, this advantage is based on the assumption that experienced forensic examiners could consistently perceive iris crypts from the given images. The foundation of crypt-based identity recognition would be questionable if different forensic examiners drew totally different conclusions from the same iris pair. In this paper, we examine this assumption through an experiment in which human observers were asked to annotate the crypts they see in gray-scale iris images. The results are studied to determine how the human visual system performs in crypt identification. To our best knowledge, there has been no previous effort in collecting manual annotation data or evaluating the perceptual consistency of crypts. The study in this paper has two different purposes. The first one is to provide empirical evidence to support a human-in-theloop framework for crypt-based iris recognition. It also sets a baseline for evaluating the performance of automatic crypt detection and matching algorithms in the future. The paper is organized as follows. Section 2 introduces background knowledge of crypts and the crypt-based iris recognition framework. Section 3 describes our dataset, session plan and the annotation software. The analysis of the annotation results is presented in Section 4. Section 5 summarizes the paper and presents recommendations for future work.

2 2. Background According to [4], crypts are a sequence of openings located near either side of the collarette on the anterior layer of the iris. They play a role in keeping the stroma and tissues on the posterior layer lubricated. Crypts are significant visible features of the iris because their formation relates to both pigmentation and the surface structure [3]. When imaged in the near infrared, crypts appear as areas that are consistently darker than their surroundings. Figure 1 shows examples of iris images with multiple visible crypts. All images used in this paper are acquired under near infrared illumination. Figure 2: System framework with manual inspection (a) (b) (c) (d) Figure 1: Examples of visible crypts The major motivation to develop visible feature-based iris recognition is to aid the law enforcement applications that require human supervision in the process. Figure 2 shows the framework of a semi-automatic subject identification system where manual inspection is added. In this framework, the system retrieves a short list of matching candidates that have the highest similarity scores and automatically annotates common crypt features that contribute to the overall similarity. During manual inspection, the human examiners select the true matching image from the retrieved list or declare a new subject by visually evaluating the similarities between the annotated common feature pairs. Compared to the traditional fully-automatic iris recognition, the results of a human-supervised system are more reliable and more convincing to the general public if we can prove that the human perception of iris crypts are consistent enough to discriminate different irises. 3. Experiment 3.1. Data All the iris images are taken from the LG4000 iris camera under NIR lighting [5]. We considered using either the eye images directly or the iris strips that are segmented and normalized from eye images. The sizes of eyes and the amount of dilation vary in the acquired eye images. To reduce the complications incurred by these differences as much as possible, we use the normalized grey-scale iris strips as the input images for annotation. The regions occluded by eyelashes or eyelids are masked by solid yellow color, and the annotators are instructed to ignore them. We hand-selected a subset of images that are of good focus, decently accurate segmentation and relatively small occlusion area. Our study uses a total of 124 images from 62 different irises with 2 images for each iris. The images are presented to participants in 3 sessions by the noticeability of crypts and difficulties of annotation as explained in table 1. The test images are labeled as easy or challenging before experiments by the author to describe the noticeability of crypts in each image. As illustrated in figure 3(b), the challenging images contain border-case crypts whose occurrence or exact shapes are difficult to decide. The credibility of labeling is limited by the author s subjective observation, so in addition to the noticeability, the images are also selected by the annotation consistency measured from previous sessions. The images presented in multiple sessions help us observe the behavior changes between sessions Sessions The experiments were carried out in three sessions, each of which contained images and the time cost of each session varied between 1 and 2 hours for different partici-

Session # of images Description 1 60 Easy images with relatively noticeable crypts 2 68 28 easy images from session 1 with poor annotation consistency and 40 challenging images with low-quality

3 Session # of images Description 1 60 Easy images with relatively noticeable crypts easy images from session 1 with poor annotation consistency and 40 challenging images with low-quality crypts easy images randomly from session 1, 20 challenging images from session 2 with relatively high annotation consistency, and new images mixed of 8 easy and 16 challenging. image at the bottom. To annotate crypts, the participants hold down the mouse button and use it as a pen to paint the regions that exactly cover the crypts. The red boundaries in the iris image and the connected regions in the feature image are updated automatically with the annotators drawing. Small holes within donut-shaped annotations are automatically filled in post-processing. A good annotator is expected to be careful with the crypt boundaries so that the red boundaries perfectly overlap with the observed crypt boundaries. Table 1: Test images in three sessions (a) (b) Figure 4: User interface of the annotation software Figure 3: Image classification by crypts noticeability. (a) is an easy image. (b) is a challenging image. pants. To reduce the likelihood of fatigue and resulting poor annotations, participants were asked to complete each session in two separate time slots lasting less than 1 hour. We solicited volunteers from the students and staff on the Notre Dame campus to participate in our experiments. We had 23 volunteers in session of them continued to participate in session 2, and 20 of them finished all three. All participants were required to watch a 6-minute tutorial video about how to operate the annotation software. It also gave participants a high-level standard for identifying crypts: image regions that are consistently darker than their surroundings. Participants were instructed to ignore very small candidate regions and those located too close to the pupil or limbus boundary because they are either not discriminatory or not stable enough Software We implemented the annotation software, and the user interface is shown in figure 4. Participants make annotations on the grey-scale normalized iris image at the top, and the annotation results are recorded as a binary feature 4. Results 4.1. Inter-Participant Perceptual Consistency Inter-participant perceptual consistency measures the similarities between annotations from different annotators for the same image. For each image I we calculated a consensus image I c in which each bit is set if it receives votes from more than 50% of participants as a crypt pixel. The inter-participant consistency for the given image I is measured by the consistency ratio θ: θ = average # of set bits in (I k Ic ) average # of set bits in (I k I c ) Where I k is the annotation result from the kth participant, and symbols and denote the logic and and exclusive or respectively. Higher values of θ indicate better inter-participant perceptual consistency. Figure 5 plots the θ values calculated for all image at each session. The ideal value for θ is + when all annotations are identical. The maximum θ we obtained from the 124 testing images is Although it is difficult to identify a decision threshold above which the participants perception of iris crypts is sufficiently consistent for identity recognition, we found in figure 6 that the level of consistency is higher (1)

4 4.2. Inter-Iris Perceptual Discrimination In this section, we evaluate the discriminatory ability of the annotated crypts in spite of the inter-participant inconsistency we observed in the last section. An all-to-all matching is conducted to simulate the identity verification based on a single decision threshold. We also conducted one-to-all matching to simulate the identification scenario using each annotation image for identity query Verification We use the overlapping percentage η to measure the similarity between two crypt annotation images I k and I l : Figure 5: Average consistency ratios for each image for easy images, which are the ones containing easily noticeable crypts as in figure 3(a). η = # of set bits in (I k Il ) # of set bits in (I k Il ) The head tilt during image acquisition causes rotational horizontal shift in the normalized iris image, and other factors such as eye movement, pupil dilation and segmentation error may result in vertical shift. To accommodate the position shifts of crypts, in calculating η we move I k both horizontally and vertically to find the position where I k and I l have the maximum number of overlapping bits. Each annotation image is considered independently in matching. Two annotation images form a matching pair if they are from the same eye. Intuitively the similarity between matching pairs is higher than that of nonmatching pairs, so the value of η is higher for matching pairs. Figure 7 shows the matching score distribution obtained from 232,965 matching and 10,637,785 nonmatching pairs. Because we have many more nonmatching pairs in experiments, the distributions are normalized to the same height for the ease of comparison. (2) Figure 6: Consistency ratios of each noticeability category A major part of annotation inconsistency comes from crypt boundaries. Of all annotation images, we found a total of 4,821,598 pixels that are different from the consensus image I c, of which 2,992,173 (62.06%) are from regions connected to (and are thus very likely to be surrounding) the crypts in I c. The inconsistent decisions on crypt boundaries may be caused by lack of training and experience. The differences between over-conservative and over-aggressive labeling behavior around crypt boundaries contributes considerably to the overall inter-participant inconsistencies. Figure 7: The match and nonmatch score distributions for an all-to-all matching experiment A reliable identity verification system has an overlapping area as small as possible between the score distributions of

5 matching and nonmatching pairs. Larger overlapping area means higher possibilities of false accept or false reject errors. Although the two peaks in figure 7 are separate, the overlapping area prevents it from being a reliable biometric mode in automatic identity verification when a single decision threshold is used to determine a positive or negative response for the identity claim. In observation we found that images with fewer crypt pixels are more likely to yield low genuine matching score η compared to those with more crypt pixels. Figures 8 plots the joint distribution between the number of crypt pixels and the average genuine matching score for each image. The number of crypt pixel of I is estimated as the average of all annotations {I k }, and the average genuine matching score is obtained by matching all images in {I k } against {I k } {I k } where {I k } are the annotations of the other image of the same iris. We tested the statistical independence between the matching score η and the number of crypt pixels in figure 8. The null hypothesis is that the matching score for a given image is independent of the number of crypt pixels in it. The p-value obtained from Pearsons chi-squared test of independence is , which is well below 0.05, so the null hypothesis is rejected, which proves the existence of correlation between the number of crypt pixels in an image and the scale of matching scores it could yield against other images of the same iris. The correlation coefficient between them is with p-value at Given the noise induced by annotation inconsistencies, the correlation is significant. The number of crypt pixels is an intrinsic property that varies in a wide range for different iris images, so it explains why using a single decision threshold for using the overlapping percentage as the matching score did not produce satisfying performance for identity verifications. It is part of our future work to model the correlation between the number of crypt pixels and matching scores to develop a more discriminatory similarity metric Identification In this section, we measure the crypts inter-iris perceptual discrimination independently for each image by conducting the closed-set identification where the identity of a query image is determined by the retrieved candidates who have the highest matching scores. Unlike identity verification, the identification decision of the given image is not affected by the range of similarity scores for other query images that are likely to have different numbers of crypt pixels. The system provides a correct identification as long as the query image generates a higher similarity score η with genuine matching images in the database than those generated with nonmatching images, even if the values of genuine matching scores are very small or the imposter matching scores are large. Although open-set identification is more popular in practice, the goal of this paper is to study the human perception of crypts, and the close-set identification serves this purpose well. Figure 8: Scatter plot of average genuine matching score and the number of crypt pixels in images Figure 9: CMC of identification by crypt annotations We conducted one-to-all matching using each crypt annotation image as the query image and match it against all other annotation images from all participants for all iris images. The performance of the closed-set identification is measured by the cumulative match characteristics (CMC), which plots how often a genuine matching template appears in the top rank candidate lists as the number of retrieved candidates increases. Although there are inter-participant

6 perceptual differences for each iris image as discussed in section 4.1, figure 9 shows that the identification achieves an overall identification rate of 89% at rank 1 and nearly 95% at rank 10 for all 4088 query attempts against the database composed of all other annotation images. We expect this performance to be considerably improved if the participants receive formal training before the annotation task. A comprehensive training process is likely to reduce the inconsistent opinions about border-case crypts and crypt boundaries. It would also help if annotators were more detailoriented about crypt boundaries in annotation, which is the major source of inter-participant inconsistency. Figure 10: Identification fail rate at rank 10 of each noticeability category Similar to the case of consistency ratio θ, figure 10 shows that the noticeability of crypts also affects the performance of identification. We compared the fail rate at rank 10 for each image calculated by using all annotations of the given image as the query input. According to the results, 171 of the 210 (81.43%) failed queries at rank 10 are from challenging images while the number of queries from easy images is 968 more than that of challenging images. 5. Conclusion and Future Work This paper studied human perception of iris crypts by analyzing the annotation data we collected from 23 volunteers. The experiments are carried out in three sessions with 124 images pre-labeled as easy and challenging by the noticeability of crypts. The human perception of crypts is evaluated in two aspects, namely inter-participant consistency and inter-iris discrimination. The inter-participant consistency measures the similarities between different annotations of the same iris image. We found that the interparticipant consistency is higher for images with easily noticeable crypts and that the boundary areas of crypts are the major source of inconsistency in annotations. Despite the existence of perceptual differences, we found through the simulation of closed-set identification that the intra-iris difference is sufficiently small for nearly 95% of query images to retrieve genuine matching irises at top ranks. Because the range of similarity scores generated from the current matcher is statistically significantly correlated with the number of crypt pixels in the image, a better matcher that produces a consistent range of scores needs to be developed before iris crypts be applied reliably in identity verification. Also we suggest developing a training process focusing on the decision of crypt boundaries in the future to reduce the dispute over boundary locations. The experiments showed considerable differences in consistency and discrimination between the two noticeability classes. We suggest that it is possible for some irises to naturally have shallower crypts than others, which makes them less reliable for crypt-based recognition. We have attempted to find the correlation between the noticeability and some properties such as image intensity and gradient, but these features are proved to be nondiscriminatory in experiments. We will continue working on an automatic noticeability evaluation in our future work. If challenging images could be automatically identified at acquisition, we may be able to increase the accuracy by applying pre-processing techniques or merging the results of multiple recognition methods. References [1] J. Daugman. How iris recognition works. IEEE Trans. on Circuits and Systems for Video Technology, 14(1):21 30, January [2] J. Daugman. Results from 200 billion iris cross-comparisons. Technical report, University of Cambridge, [3] L. Flom and A. Safir. Iris recognition system. US Patent 4,641,349, [4] D. H. Gold and R. Lewis. Clinical eye atlas. Oxford University Press, [5] LG. Iris access [6] L. Ma, T. Tan, Y. Wang, and D. Zhang. Efficient iris recognition by characterizing key local variations. IEEE Trans. on Image Processing, 13(6): , [7] J. D. Mira and J. Mayer. Image freature extraction for application of biometric identification of iris - a morphological approach. Proc. of SIBGRAPI, , [8] F. Shen and P. J. Flynn. Iris matching by crypts and anticrypts. Proc. of IEEE Converence on Technologies for Homeland Security, pages , November [9] F. Shen and P. J. Flynn. Using crypts as iris minutiae. Proc. SPIE, 8712, May [10] M. S. Sunder and A. Ross. Iris image retrieval based on macro-features. Proc. of ICPR, , [11] W. Yang, L. Yu, and K. Wang. Iris recognition based on location of key points. Proc. ICBA, pages , July 2004.

Critique: Efficient Iris Recognition by Characterizing Key Local Variations

Critique: Efficient Iris Recognition by Characterizing Key Local Variations Authors: L. Ma, T. Tan, Y. Wang, D. Zhang Published: IEEE Transactions on Image Processing, Vol. 13, No. 6 Critique By: Christopher