HANDBOOK OF IRIS RECOGNITION PDF

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This year, as part of their Advances in Computer Vision and Pattern Recognition series, Springer has published Handbook of Iris Recognition. Request PDF on ResearchGate | Handbook of Iris Recognition | This chapter discusses the performance of five different iris segmentation algorithms on. The first book of its kind, providing complete coverage of the key subjects in iris recognition, from sensor acquisition to matching. ▷ With contributions from.


Handbook Of Iris Recognition Pdf

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Topics and features: with a Foreword by the “father of iris recognition,” Professor Included format: EPUB, PDF; ebooks can be used on all reading devices. Presents complete coverage of the key subjects in iris recognition, from ISBN ; Digitally watermarked, DRM-free; Included format: PDF. Iris Recognition. - What is Iris as a Biometric? - How to extract the iris region? - Iris Normalization, Feature Extraction and Matching. By. Shireen Y. Elhabian.

This chapter presents a short summary of quality factors traditionally used in iris recognition systems. It further introduces new metrics that can be used to evaluate iris image quality. The performance of the individual quality measures is analyzed, and their adaptive inclusion into iris recognition systems is demonstrated. Three methods to improve the performance of biometric matchers based on vectors of quality measures are described.

For all the three methods, the reported experimental results show significant performance improvement when applied to iris biometrics. This confirms that the newly proposed quality measures are informative in the sense that their involvement results in improved iris recognition performance.

Schmid, J. Zuo, F. Nicolo, H. Wechsler Chapter 6. These experimental evaluations employed different vendor technologies and experimental specifications, but yield consistent results in the areas where the specifications intersect.

In the ICE , participants were allowed to submit quality measures. We investigate the properties of their quality submissions. Jonathon Phillips, Patrick J.

Why use biometrics?

Flynn Chapter 7. Methods for Iris Segmentation Abstract Under ideal image acquisition conditions, the iris biometric has been observed to provide high recognition performance compared to other biometric traits. Such a performance is possible by accurately segmenting the iris region from the given ocular image. This chapter discusses the challenges associated with the segmentation process, along with some of the prominent iris segmentation techniques proposed in the literature.

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The methods are presented according to their suitability for segmenting iris images acquired under different wavelengths of illumination. Furthermore, methods to refine and evaluate the output of the iris segmentation routine are presented.

The goal of this chapter is to provide a brief overview of the progress made in iris segmentation. Raghavender Jillela, Arun A. Ross Chapter 8.

Iris scans

Iris Recognition with Taylor Expansion Features Abstract The random distribution of features in an iris image texture allows to perform iris-based personal authentication with high confidence. In this chapter we describe three iris representations. The first one is a phase-based iris texture representation which is based on a binarized multi-scale Taylor expansion. The second one describes the iris by using the most significant local extremum points of the first two Taylor expansion coefficients.

The third method is a combination of the first two representations. For all methods we provide efficient similarity measures which are robust to moderate iris segmentation inaccuracies. Using three public iris datasets, we show a the compact template size of the first two representations and b their effectiveness: the first two representations alone perform well already, but in combination, they outperform state-of-the-art iris recognition approaches significantly.

Application of Correlation Filters for Iris Recognition Abstract Excellent recognition accuracies have been reported when using iris images, particularly when high-quality iris images can be acquired.

The best-known strategy for matching iris images requires segmenting the iris from the background, converting the segmented iris image from Cartesian coordinates to polar coordinates, using Gabor wavelets to obtain a binary code to represent that iris and using the Hamming distances between such binary representations to determine whether two iris images match or do not match.

However, some of the component operations may not work well when the iris images are of poor quality, perhaps as a result of the long distance between the camera and the subject. One approach to matching images with appearance variations is the use of correlation filters CF.

In this chapter, we discuss the use of CFs for iris recognition.

CFs exhibit important benefits such as shift-invariance and graceful degradation and have proven worthy of consideration in other pattern recognition applications such as automatic target recognition. In this chapter, we will discuss the basics of CF design and show how CFs can be used for iris segmentation and matching.

Smereka Chapter Currently, more than 60 million people have been mathematically enrolled by this algorithm. Its computational advantages, including high matching speed, predictable false acceptance rates and robustness against local brightness and contrast variations, play a significant role in its commercial success. To further these computational advantages, researchers have modified this algorithm to enhance iris recognition performance and recognize other biometric traits e.

Many scientific papers on iris recognition have been published, but its theory is almost completely ignored. In this chapter, we will report our most recent theoretical work on the IrisCode. Robust and Secure Iris Recognition Abstract Iris biometric entails using the patterns on the iris as a biometric for personal authentication.

It has additional benefits over contact-based biometrics such as fingerprints and hand geometry. However, iris biometric often suffers from the following three challenges: ability to handle unconstrained acquisition, privacy enhancement without compromising security, and robust matching. This chapter discusses a unified framework based on sparse representations and random projections that can address these issues simultaneously.

Furthermore, recognition from iris videos as well as generation of cancelable iris templates for enhancing the privacy and security is also discussed. Jaishanker K. However, previous research has shown that matching performance is not invariant to iris color and can be improved by imaging outside the NIR spectrum.

Building on this research, we demonstrate that iris texture increases with the frequency of the illumination for lighter colored sections of the iris and decreases for darker sections. Using registered visible light and NIR iris images captured using a single-lens multispectral camera, we illustrate how physiological properties of the iris e. We introduce a novel iris code, Multispectral Enhanced irisCode MEC , which uses pixel-level fusion algorithms to exploit texture variations elicited by illuminating the iris at different frequencies, to improve iris matcher performance, and reduce Failure To Enroll FTE rates.

Finally, we present a model for approximating an NIR iris image using features derived from the color and structure of a visible light iris image. The simulated NIR images generated by this model are designed to improve the interoperability between legacy NIR iris images and those acquired under visible light by enabling cross wavelength matching of NIR and visible light iris images.

Handbook of Iris Recognition

Mark J. Burge, Matthew Monaco Chapter Iris Segmentation for Challenging Periocular Images Abstract This chapter discusses the performance of five different iris segmentation algorithms on challenging periocular images.

The goal is to convey some of the difficulties in localizing the iris structure in images of the eye characterized by variations in illumination, eyelid and eyelash occlusion, de-focus blur, motion blur, and low resolution. The five algorithms considered in this regard are based on the a integro-differential operator; b hough transform; c geodesic active contours; d active contours without edges ; and e directional ray detection method.

Ross, Vishnu Naresh Boddeti, B. Periocular Recognition from Low-Quality Iris Images Abstract Definitions of the periocular region vary, but typically encompass the skin covering the orbit of the eye.

Especially in cases where the iris has not been acquired with sufficient quality to reliably compute an IrisCode, the periocular region can provide additional discriminative information for biometric identification.

Why use biometrics?

We investigate periocular recognition on the FOCS dataset using three distinct classes of features: photometric, keypoint, and frequency-based. We examine the performance of these features alone, in combination, and when fused with classic IrisCodes.

Josh Klontz, Mark J.

Burge Chapter In this scope, one active research topic seeks to use as main trait the ocular region acquired at visible wavelengths, from moving targets and large distances. Under these conditions, performing reliable recognition is extremely difficult, because such real-world data have features that are notoriously different from those obtained in the classical constrained setups of currently deployed recognition systems.

Next, it summarizes the most relevant research conducted in the scope of visible wavelength iris recognition and relates it to the concept of periocular recognition, which is an attempt to augment classes separability by using—apart from the iris—information from the surroundings of the eye. Finally, the current challenges in this topic and some directions for further research are discussed. Design Decisions for an Iris Recognition SDK Abstract Open-source software development kits are vital to iris biometric research in order to achieve comparability and reproducibility of research results.

In addition, for further advances in the field of iris biometrics the community needs to be provided with state-of-the-art reference systems, which serve as adequate starting point for new research. This chapter provides a summary of relevant design decisions for software modules constituting an iris recognition system.

This book is an essential resource for anyone wishing to improve their understanding of iris recognition technology. With this clear labour of love, Burge and Bowyer have made an outstanding contribution to our science of automated human recognition, for which they and the contributors are to be strongly congratulated. For anyone interested in iris recognition, this book is indispensable.

JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Computer Science Image Processing. Show next edition. The first book of its kind, providing complete coverage of the key subjects in iris recognition, from sensor acquisition to matching With contributions from numerous experts in iris biometrics from government, industry and academia, the definitive source of iris biometric information Provides an authoritative presentation of the business decisions involved in developing and fielding an iris biometric system see more benefits.

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FAQ Policy. About this book This authoritative collection introduces the reader to the state of the art in iris recognition technology. Show all. From the book reviews:Bowyer, Mark J.

Iris Spoofing: The goal is to convey some of the difficulties in localizing the iris structure in images of the eye characterized by variations in illumination, eyelid and eyelash occlusion, de-focus blur, motion blur, and low resolution. PAGE 1. Flynn, P. If you hate having to carry a jangling bunch of jailer's keys wherever you go, imagine how cool it would be if you could unlock your door just by staring at it for a couple of seconds!

The second one describes the iris by using the most significant local extremum points of the first two Taylor expansion coefficients. From that moment onward, McCurry and his team felt that they had located the family of Gula.