How iris recognition works

By John G. Daugman. IEEE transactions on circuits and systems for video technology (Print), 2004

3,372 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d9a863db269b2d8ee317f32280d1a52f15fc3bb2

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Citations in the corpus

KinectFaceDB: A Kinect Database for Face Recognition (2014, background): “In comparison to other popular biometric traits (such as fingerprint [17] and iris [18]), biometric recognition (identification/verification) based on face requires the least user cooperation and thus can be applied in many advanced conditions (e.”
Gender-from-Iris or Gender-from-Mascara? (2017, methodology): “Gabor filtering is done as part the standard approach to creating the ”iris code” [3, 4].”
Face Recognition with Local Binary Patterns (2012, methodology): “…methods like Personal Identification Number (PIN)-codes, passwords and Identification-cards, which according to [1],[2] could be exposed to security attacks, but also extremely reliable methods of biometric person identiﬁcation, like ﬁngerprint analysis [3] and retinal or iris scans [4].”
Automated human identification using ear imaging (2012, background): “7 illustrates set of such automatically normalized, segmented and enhanced ear images employed for the feature extraction and classification.”
Image understanding for iris biometrics: A survey (2008, methodology): “More detail on the focusing procedure is given in the appendix of [33].”
New Methods in Iris Recognition (2007, background): “Dorairaj et al. [5] approached this by seeking the gaze parameters that optimize the value of an integro-differential operator [2], [4], which detects circular boundaries.”
A survey of biometric recognition methods (2004, background): “Its complex pattern can contain many distinctive features such as arching ligaments, furrows, ridges, crypts, rings, corona, freckles and a zigzag collarette [7].”
Online finger-knuckle-print verification for personal authentication (2010, background): “With the rapid development of computer techniques, in the past three decades researchers have exhaustively investigated the use of a number of biometric characteristics, including fingerprint [1–3], face [4,5], iris [6,7], retina [8,9], palmprint [10–16], hand geometry [17–19], hand vein [20,21], fi”
Person Recognition in Personal Photo Collections (2017, background): “iris patterns have been considered strong visual biometric cues [45], [46].”
A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation (2018, methodology): “In this paper, we adopt decidability index (DI) to measure the degree of separation between the two distributions [35]: where µ p and µ n are the mean of the response distribution around positive and negative clicks, respectively; σ p and σ n denote the variance of the two distributions.”
Toward Accurate and Fast Iris Segmentation for Iris Biometrics (2009, neutral): “ITH the increasing demands of security in our daily life, iris recognition has rapidly become a hot research topic for its potential application values in personal identification [1], [3], [8], [9], [12].”
Robust Scheme for Iris Presentation Attack Detection Using Multiscale Binarized Statistical Image Features (2015, methodology): “Then, the segmented iris is normalized using Daugman’s rubber sheet expansion technique [22] available within OSIRIS V4.”
Study of Subjective and Objective Quality Assessment of Video (2010, background): “motion analysis [48], computational stereo [49], and human biometrics [50], [51].”
Palmprint identification using feature-level fusion (2006, background): “Some may further ask why the imposter distribution of IrisCode follows binomial distribution if the sum of correlated Bernoulli trails does not give binomial distribution.”
Recognition of Human Iris Patterns for Biometric Identification (2003, background): “The Daugman system is patented [5] and the rights are now owned by the company Iridian Technologies.”
An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets (2019, methodology): “During the past decades, machine vision has greatly become a new research field of electronics in which different applications are performed including medical image processing and diagnosis [1, 2], radar image analysis [3–17], biometrics and iris recognition [18, 19], human detection [20, 21].”
Introduction to Biometrics (2011, background): “The complex iris texture carries very distinctive information useful for personal recognition [ 4 ].”