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A Robust Method for Addressing Pupil Dilation in Iris Recognition

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A Robust Method for Addressing Pupil Dilation in Iris Recognition A ROBUST METHOD FOR ADDRESSING PUPIL DILATION IN IRIS RECOGNITION By Raghunandan Pasula A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Computer Science – Master of Science 2016 ABSTRACT A ROBUST METHOD FOR ADDRESSING PUPIL DILATION IN IRIS RECOGNITION By Raghunandan Pasula The rich texture of the iris is being used as a biometric cue in several human recognition systems. Iris recognition systems are fairly robust to small changes in illumination and pose. However there are a number of factors that still adversely affect the performance of an iris matcher. These include occlusion, large deviation in gaze, low image resolution, long acquisition distance and pupil dilation. Large differences in pupil size increases the dissimilarity between iris images of the same eye. In this work, the degradation of match scores due to pupil dilation is systematically studied using Hamming Distance histograms. A novel rule-based fusion technique based on the aforemen- tioned study is proposed to alleviate the effect of pupil dilation. The proposed method computes a new distance score at every pixel location based on the similarities between IrisCode bits that were generated using Gabor Filters at different resolutions. Experiments show that the proposed method increases the genuine accept rate from 76% to 90% at 0.0001% false accept rate when comparing images with large differences in pupil sizes in the WVU-PLR dataset. The proposed method is also shown to improve the performance of iris recognition on other non-ideal iris datasets. In summary, the use of multi-resolution Gabor Filters in conjunction with a rule-based integration of decisions at the pixel (bit) level is observed to improve the resilience of iris recognition to differences in pupil size. ACKNOWLEDGEMENTS I would like to thank Dr. Arun Ross for his continued support and guidance through out my student career. No amount of thanks would be sufficient to describe the support extended by the family members by just being there for me and supporting me through the degree program. Special thanks to Dr. Eric Torng and Katherine Trinklein for helping me through the tough times. This work is dedicated to all the gurus who spend significant amount of effort and time to impart knowledge to the world. iii TABLE OF CONTENTS LIST OF TABLES ....................................... vi LIST OF FIGURES ....................................... vii CHAPTER 1 INTRODUCTION ............................... 1 1.1 Biometrics . 1 1.2 Eye anatomy . 4 1.2.1 Layers of eye . 5 1.2.2 Apparent iris texture . 9 1.2.3 Pupil dynamics . 12 1.3 Iris biometric system . 13 1.4 Challenges in Iris recognition . 20 1.5 Objectives of this work . 25 CHAPTER 2 MOTIVATION AND PREVIOUS WORK .................. 26 2.1 Motivation . 26 2.2 Previous work . 28 2.2.1 Minimum wear and tear model . 28 2.2.1.1 Wyatt 2000 . 28 2.2.1.2 Yuan and Shi 2005 . 31 2.2.1.3 Wei et al. 2007 . 32 2.2.2 3-D anatomical model . 33 2.2.2.1 Francois et al. 2007 . 33 2.2.2.2 Clark et al. 2012 . 34 2.2.3 Gejji et al. 2015 . 36 2.2.4 Other Deformation models . 36 2.3 Bit matching . 37 CHAPTER 3 COLLECTION OF DATABASE ........................ 39 3.1 Motivation . 39 3.2 Data acquisition protocol . 39 3.3 Description . 40 3.4 Impact of pupil dilation . 42 CHAPTER 4 PROPOSED METHODS ........................... 44 4.1 Multi-resolution Gabor filter encoding . 44 4.2 Typical IrisCode matcher . 45 4.3 Histogram of matching patterns . 46 4.4 Fusion . 48 4.4.1 Rule based Fusion . 49 4.4.2 Classifier based Fusion . 52 4.5 Experiments and Results . 52 iv 4.6 Examples . 56 CHAPTER 5 SUMMARY .................................. 60 5.1 Summary . 60 BIBLIOGRAPHY ........................................ 61 v LIST OF TABLES Table 1.1 Wavelength range for visible, NIR and SWIR spectrum . 9 Table 3.1 Demographics distribution . 41 Table 3.2 Eye color information . 41 Table 4.1 Logical operations used to combine the output of multiple IrisCodes. 52 vi LIST OF FIGURES Figure 1.1 Figure showing the external anatomy of the human eye in the RGB spectrum. The focus of this thesis is on the iris, which is the annular textured structure situated between the pupil and the sclera. The iris is typically imaged in the near infrared (NIR) spectrum and not in the RGB spectrum. 2 Figure 1.2 A biometric system, during the, enrollment stage adds a template belonging to a new user into the Gallery. 3 Figure 1.3 Sagittarial cross-section of iris. Image published here with permission from [1] 6 Figure 1.4 Different layers of iris when looking into sagittal axis. 7 Figure 1.5 Location of sphincter and dilator muscles that control pupil constriction and dilation, respectively. 8 Figure 1.6 Path from light source to the eye . 10 Figure 1.7 Figure showing wavelengths of electro-magnetic spectrum relevant to iris biometrics . 11 Figure 1.8 Absorption spectrum of (a) liquid water [2] and (b) melanin [3] at different wavelengths of electromagnetic spectrum . 11 Figure 1.9 Example of blue, yellowish and dark brown iris images that contain low, moderate and high concentration of melanin, respectively. 12 Figure 1.10 Dark iris in (a) imaged at (b) 470nm, (c) 520nm, and (d) 700nm and (e) NIR wavelengths. 12 Figure 1.11 Examples of factors influencing the size of the pupil. 13 Figure 1.12 Components of a typical iris recognition system . 14 Figure 1.13 (a) Real part and (b) Imaginary part of a Gabor filter . 18 Figure 1.14 (a) Original acquired Image (b) Segmentation output (c) Normalized image (d) Corresponding mask image (e) IrisCode generated by encoding the nor- malized image using Masek’s method. [4] . 19 Figure 1.15 Examples of non ideal iris images. (a) and (b) Non-uniform illumination, (c) and (d) Eyelid occlusion, (e) Eyelash occlusion, (f) Motion blur. 22 Figure 1.16 Examples of off-axis iris images. 22 vii Figure 1.17 Few examples of eye diseases that impact iris recognition (a) Polycoria - Multiple pupil openings (b) Coloboma - Tear in iris (c) Severe cataract - Thickening of lens (looses transparency). Although the images are shown in RGB, some of these diseases can also impact the NIR images. 24 Figure 2.1 (a) and (b) Iris image with moderate pupil size and the corresponding nor- malized iris image. (c) and(d) Iris image with large pupil size and the cor- responding normalized iris image. Highlighted regions in (c) and (d) do not align correctly. Images from [5]. 27 Figure 2.2 Iris images with dilation ratios of (a) 0.3478 and (b) 0.6545. Images from [6]. 27 Figure 2.3 Iris mesh work proposed by Rohen [7]. Image from [7]. 29 Figure 2.4 θo is the angle between starting point of the fiber arc on pupillary boundary and ending point on limbic boundary. 30 Figure 2.5 Optimum arcs derived by Wyatt [8] for θ = 100◦, and pupil diameter 1.5, 4.0 and 7.0 mm . 30 Figure 2.6 Normalization model proposed by Yuan and Shi. Image from [9]. 31 Figure 3.1 Image sequence capture starts at t0 = 0. After approximately 10 seconds, at t1, the light source is turned on illuminating the eye for 10 more seconds [t1,t2]. At t2 the light source is turned off and remains off for 10 more seconds [t2,t3]. The video capture is stopped at t3...................... 40 Figure 3.2 Sample images from the dataset . 41 Figure 3.3 Distribution of pupil dilation ratios in the dataset. They range from 0.2177 to 0.6367. 42 Figure 3.4 Distribution of genuine Hamming distance scores as a function of dilation differences. (a) D1 D2 and (b) R1 R2 ................... 43 | − | | − | Figure 4.1 A normalized image is encoded using multi-scale filters to result in an IrisCode set along with a mask showing valid bits in each IrisCode. This mask is same for all the codes in the IrisCode set . 45 Figure 4.2 A normalized image and its corresponding IrisCode generated using 3 filters. These filters encode the image at multiple scales. 45 Figure 4.3 A typical iris matcher. Match scores are computed independently at each scale which are then fused at score level to result in a final distance score. 47 viii Figure 4.4 Distribution of multi-filter decisions for genuine matching cases for a single subject. 48 Figure 4.5 Distribution of multi-filter decisions for randomly selected impostor match- ing cases . 49 Figure 4.6 Comparison of distributions of possible multi-filter decisions for genuine and impostor matching cases . 50 Figure 4.7 The proposed iris matcher sequentially combines the results at multiple scales and generates a single decision result. 51 Figure 4.8 Flowchart depicting Method 1 and its corresponding truth table . 53 Figure 4.9 Flowchart depicting Method 2 and its corresponding truth table . 54 Figure 4.10 Flowchart depicting Method 3 and its corresponding truth table . 55 Figure 4.11 (a) ROCs for full data. The genuine and impostor score distributions are plotted for (b) Method 1, (c) Method 2 and (d) Method 3. 56 Figure 4.12 ROCs generated by using the genuine scores for pairs whose pupil dilation ratio differences are (a) small, (b) medium and (c) large. The impostor dis- tributions are held the same across all the cases. 57 Figure 4.13 The histogram of genuine and impostor scores using Masek’s method and after fusion of match scores from Masek’s method and proposed Method 1.
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