Handbook of Iris Recognition
Iris recognition is both a technology already in successful use in ambitious nation scale applications and also a vibrant, active research area with many difficult and exciting problems yet to be solved. This chapter gives a brief introduction to iris recognition and an overview of the chapters in the Second Edition of the Handbook of Iris Recognition. 1.1 The Current State of Iris Recognition The use of iris texture analysis for biometric identification is now well established. The United Arab Emirates has been using iris recognition for border control since 2001 [23] and famously claims "some 2.7 billion iris cross comparisons being done every day" [8] India is using iris recognition as part of its Unique ID program, along with fingerprint recognition. By 2016, the number of people enrolled in India's Unique IDprogramexceeded three times the population size of theUnitedStates [15] The National Institute of Standards and Technology (NIST) in the United States has produced a series of seven IREX reports on iris recognition technology, with more planned for the future [27] A market analysis report predicts that the worldwide market for iris recognition technology will experience a cumulative annual growth rate of 23.5% over the period 2015 2019 [29] The various high profile uses of iris recognition, the sequence of IREX technical reports by NIST, and the rosy market forecast for iris recognition technology, might lead casual observers to think that iris recognition is a solved problem, and no longer an active research area. This would be far from the truth. The transition of iris recognition from laboratory technology K.W. Bowyer (B) University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN 46556, USA e mail: kwb@cse.nd.edu M.J. Burge Noblis, Falls Church, VA, USA e mail: mark.burge@noblis.org (c) Springer Verlag London 2016 K.W. Bowyer and M.J. Burge (eds. Handbook of Iris Recognition, Advances in Computer Vision and Pattern Recognition, DOI 10.1007/978 1 4471 6784 6_1 1 2 K.W. Bowyer and M.J. Burge to nation scale applications has actually spurred the need for research to solve many difficult and interesting problems. The research frontiers in iris recognition are moving to more difficult and demanding sorts of problems, where results need to prove out on larger and more real world datasets. As just one example of this, as iris recognition begins to be used in large scale commercial applications, the problem of liveness testing being sure that the iris image comes from a live iris rather than some fake iris becomes a practical and important question. The problem of liveness detection is the focus of the new chapter, contributed by AdamCzajka, added to the Second Edition. Liveness detection is also touched on in the new chapter by Galbally, Savvides, Venugopalan and Ross. The Second Edition also includes: a new chapter by Rathgeb, Uhl, Wild and Hofbauer that details the design decisions involved in implementing a software system for iris recognition; a new chapter by Bolme, Santos Villalobos, Thompson, Karakaya, and Boehnen that introduces state of the art approaches to correcting off angle iris images; a new chapter by Nigam, Vatsa and Singh discussing the range of different medical conditions of the eye that can affect iris recognition; a revised and updated chapter by Jillela and Ross on methods for iris segmentation; and a revised and updated chapter by Proenca on iris recognition in visible wavelength illumination. Even with the new and revised chapters in the Second Edition, there are some interesting current research problems that are not represented by their own chapter. One such research problem is the effects of contact lenses on iris recognition accuracy. Cosmetic contact lenses present challenges that are more complex than is widely appreciated and even plain, clear, prescription contact lenses have been documented to cause a small degradation in iris recognition accuracy [2] There is a known approach, due to Daugman [6] for detecting whether or not a person is wearing a cosmetic contact lens for which the synthetic texture is created by a "dot matrix" process. However, there are other means of manufacturing cosmetic contact lenses that are not detected by this approach. An example image representing a non dotmatrix type of cosmetic lens is shown in Fig. 1.1. Fig. 1.1 Example iris image exhibiting texture from cosmetic contact lens. Note the slivers of natural iris texture visible by the sclera boundary near the nose and by pupil boundary away from the nose. Note also that the texture pattern is not created in a dot matrix style, and so would not be detected by Daugman's method
Advances in Computer Vision and Pattern Recognition