Increased Use of Available Image Data Decreases Errors in Iris Biometrics
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INCREASED USE OF AVAILABLE IMAGE DATA DECREASES ERRORS IN IRIS BIOMETRICS A Dissertation Submitted to the Graduate School of the University of Notre Dame in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Karen P. Hollingsworth Kevin W. Bowyer, Co-Director Patrick J. Flynn, Co-Director Graduate Program in Computer Science and Engineering Notre Dame, Indiana July 2010 c Copyright by Karen P. Hollingsworth 2010 All Rights Reserved INCREASED USE OF AVAILABLE IMAGE DATA DECREASES ERRORS IN IRIS BIOMETRICS Abstract by Karen P. Hollingsworth Iris biometrics is used in a number of different applications, such as frequent flyer programs, identification of prisoners, and border control in the United Arab Emirates. However, governments interested in using iris biometrics have still found difficulties using it on large populations. Further improvements in iris recognition are required in order to enable this technology to be used in more settings. In this dissertation, we describe three methods of reducing error rates for iris biometrics. We define and employ a metric called the fragile bit distance which uses the locations of less stable bits in an iris template to improve performance. We also investigate signal fusion of multiple frames in an iris video to achieve better recognition performance than is possible using single still images. Third, we present a study of what features are useful for identification in the periocular region. Periocular biometrics is still an emerging field of research, but we antici- pate that fusing periocular information with iris information will result in a more robust biometric system. A final contribution of this work is a study of how iris biometrics performs on twins. Our experiments confirm prior claims that iris biometrics is capable of differentiating between twins. However, we additionally show that there is Karen P. Hollingsworth texture information in the iris that is not encoded by traditional iris biometrics systems. Our experiments suggest that human examination of pairs of iris images for forensic purposes may be feasible. Our results also suggest that development of different approaches to automated iris image analysis may be useful. CONTENTS FIGURES.................................... vi TABLES .................................... xi ACKNOWLEDGMENTS ........................... xii CHAPTER 1: INTRODUCTION . 1 CHAPTER2:BACKGROUND. 5 2.1 Performance of Biometric Systems . 5 2.1.1 Verification .......................... 5 2.1.2 Identification ......................... 8 2.2 EyeAnatomy ............................. 9 2.3 Early Research in Iris Biometrics . 11 2.4 Recent Research in Iris Biometrics . 18 2.4.1 Image Acquisition, Restoration, and Quality Assessment . 19 2.4.1.1 ImageAcquisition . 19 2.4.1.2 ImageRestoration . 21 2.4.1.3 ImageQuality . 22 2.4.2 ImageCompression . 26 2.4.3 Segmentation ......................... 27 2.4.3.1 ActiveContours . 27 2.4.3.2 Alternatives to Active Contours . 28 2.4.3.3 Eyelid and Eyelash Detection . 30 2.4.3.4 Segmenting Iris Images with Non-frontal Gaze . 31 2.4.4 FeatureExtraction . 33 2.4.5 Improvements in Matching . 34 2.4.6 Searching Large Biometrics Databases . 35 2.4.7 Applications.......................... 36 2.4.7.1 Cryptographic Applications . 36 2.4.7.2 Identity Cards in the U.K. 39 ii 2.4.8 Evaluation........................... 40 2.4.9 Performance under Varying Conditions . 41 2.4.10 Multibiometrics . 43 CHAPTER 3: FRAGILE BIT COINCIDENCE . 46 3.1 Motivation............................... 46 3.2 RelatedWork ............................. 50 3.2.1 Research on Fusing Hamming Distance with Added Infor- mation............................. 50 3.2.2 ResearchonFragileBits . 52 3.3 Data.................................. 54 3.4 FragileBitDistance(FBD) . 57 3.5 Score Distributions for Hamming Distance and Fragile Bit Distance 59 3.6 Fusing Fragile Bit Distance with Hamming Distance . 60 3.7 Tests of Statistical Significance . 68 3.8 Effect of Modifying the Fragile Bit Masking Threshold . 70 3.9 Discussion ............................... 76 CHAPTER 4: AVERAGE IMAGES . 77 4.1 Motivation............................... 77 4.2 RelatedWork ............................. 79 4.2.1 Video ............................. 79 4.2.2 StillImages .......................... 80 4.3 Data.................................. 81 4.4 AverageImagesandTemplates . 82 4.4.1 Selecting Frames and Preprocessing . 82 4.4.2 SignalFusion ......................... 85 4.4.3 Creating an Iris Code Template . 88 4.5 Comparison of Median and Mean for Signal Fusion . 90 4.6 How Many Frames Should be Fused in an Average Image? . 92 4.7 How Much Masking Should be Used in an Average Image? . 95 4.8 ComparisontoOtherMethods. 96 4.8.1 Comparison to Previous Multi-gallery Methods . 97 4.8.2 Comparison to Previous Log-Likelihood Method . 101 4.8.3 Comparing to Large Multi-Gallery, Multi-Probe Methods . 103 4.8.4 ComputationTime . 105 4.9 Discussion ............................... 110 CHAPTER 5: IRIS BIOMETRICS ON TWINS . 111 5.1 Motivation............................... 111 5.2 RelatedWork ............................. 113 5.3 Data.................................. 116 iii 5.3.1 FrameSelection . 117 5.3.2 Segmentation ......................... 118 5.4 Biometric Performance on Twins’ Irises . 119 5.5 Similarities in Twins’ Irises Detected by Humans . 122 5.5.1 ExperimentalSetup. 122 5.5.2 Results............................. 126 5.5.2.1 Can Humans Identify Twins from Iris Texture Alone? 126 5.5.2.2 Can Humans Identify Twins from Periocular Informa- tionAlone?........................ 126 5.5.2.3 Did Humans Score Higher on Queries where They Felt MoreCertain? ...................... 127 5.5.2.4 Is It Easier to Identify Twin Pairs Using Iris Data or PeriocularData?. 127 5.5.2.5 Did Subjects Score Better on the Second Half of the IrisTestthantheFirstHalf? . 128 5.5.2.6 Did Subjects Score Better on the Second Half of the Periocular Test than the First Half? . 129 5.5.2.7 Which Image Pairs Were Most Frequently Classified Correctly, and Which Pairs Were Most Frequently Clas- sifiedIncorrectly? . 130 5.5.2.8 Is It More Difficult to Label Twins as Twins than It Is to Label Unrelated People as Unrelated? . 132 5.6 Discussion ............................... 134 CHAPTER 6: PERIOCULAR BIOMETRICS . 136 6.1 Motivation............................... 136 6.2 RelatedWork ............................. 138 6.3 Data.................................. 140 6.4 ExperimentalMethod. 142 6.5 Results................................. 144 6.5.1 How Well Can Humans Determine whether Two Periocular Images Are fromthe Same Person or Not? . 144 6.5.2 Did Humans Score Higher when They Felt More Certain? . 144 6.5.3 Did Testers Do Better on the Second Half of the Test than theFirstHalf? ........................ 145 6.5.4 Which Features Are Correlated with Correct Responses? . 145 6.5.5 Which Features Are Correlated with Incorrect Responses? 147 6.5.6 What Additional Information Did Testers Provide? . 147 6.5.7 Which Pairs Were Most Frequently Classified Correctly, and Which Pairs Were Most Frequently Classified Incorrectly? 149 6.6 Discussion ............................... 151 iv CHAPTER7:CONCLUSIONS . 155 BIBLIOGRAPHY ............................... 157 v FIGURES 2.1 In a biometric system, the number of false accepts and the number of false rejects are related to the chosen decision criteria (Figure modeled after [27])........................... 7 2.2 Image 05495d15 from Notre Dame Dataset. Elements seen in a typical iris image are labeled here. 10 2.3 Commercial iris cameras use near-infrared illumination so that the illumination is unintrusive to humans, and so that the texture of heavily pigmented irises can be imaged more effectively. This graph shows the spectrum of wavelengths emitted by the LEDs on an LG 2200 iris camera. This camera uses wavelengths primarily between 700 and 900 nanometers. The spectral characteristics were cap- tured using spectrophotometric equipment made available by Prof. Douglas Hall of the University of Notre Dame. 13 2.4 Melanin pigment absorbs much of visible light, but reflects more of the longer wavelengths of light (Picture reprinted from [23], data from [71])................................ 14 2.5 Major steps in iris biometrics processing. (Picture reprinted from [16] with permission from Elsevier.) . 17 2.6 Kang and Park [68] and He et al. [48] use information about cam- era optics and position of the subject to estimate a point spread function and restore blurry images to in-focus images. Above is an example of (a) a blurry iris image and (b) an in-focus image of the samesubject. ............................. 23 2.7 Belcher and Du’s quality measure [7] combines information about occlusion, dilation, and texture. Above is an example of (a) a heavily occluded iris image, and (b) a less occluded image of the samesubject. ............................. 24 2.8 As iris biometrics is used for larger and more varied applications, it will have to deal with irises with various different conditions. This image shows an unusual iris (Subject 05931) with filaments of tissue extending into the pupil. 42 vi 2.9 MBGC data included near infrared iris videos captured with a Sarnoff Iris on the Move portal, shown above. Video of a sub- ject is captured as a user walks through the portal. This type of acquisition is less constrained than traditional iris cameras, how- ever, the quality of the iris images acquired is poorer. It is possible to acquire both face and iris information using this type of portal. (Picture