New Methods in Iris Recognition

By John G. Daugman. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2007

1,133 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/212167ac1eec83933ab23c046eac73a3d0b95105

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Citations in the corpus

Image understanding for iris biometrics: A survey (2008, methodology): “Recently, Daugman has studied alternative segmentation techniques to better model the iris boundaries [35].”
Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing (2008, methodology): “This system explicitly models the upper and lower eyelids with parabolic arcs whereas Daugman excludes the upper and the lower portions of the image.”
Toward Accurate and Fast Iris Segmentation for Iris Biometrics (2009, neutral): “More recently, the community have paid more and more attention on the process of nonideal iris images [4], [21], [22], [23], [24], [25], [26].”
Guessing human-chosen secrets (2012, background): “Biometrics are any means for identifying humans by difficult-to-forge physiological traits, which may be physical traits such as iris patterns [76], fingerprints [253], or facial structure [192], or behavioural traits such as a user’s voice [18], handwriting [25], or typing style [210].”
Iris Segmentation Using Geodesic Active Contours (2009, methodology): “Large-scale authentication experiments have confirmed this notion further underscoring the relevance of this biometric trait in distinguishing individuals [2], [3].”
Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition (2010, methodology): “Instead, we try to detect and eliminate the localization inaccuracies via intensity statistics [2].”
The Quest to Replace Passwords: A Framework for Comparative Evaluation of Web Authentication Schemes (2012, background): “2) Iris recognition: In rating the physical biometric of iris recognition [84], [87] for our target application”