New methods in iris recognition.
This paper presents the following four advances in iris recognition: 1) more disciplined methods for detecting and faithfully modeling the iris inner and outer boundaries with active contours, leading to more flexible embedded coordinate systems; 2) Fourier-based methods for solving problems in iris trigonometry and projective geometry, allowing off-axis gaze to be handled by detecting it and "rotating" the eye into orthographic perspective; 3) statistical inference methods for detecting and excluding eyelashes; and 4) exploration of score normalizations, depending on the amount of iris data that is available in images and the required scale of database search. Statistical results are presented based on 200 billion iris cross-comparisons that were generated from 632500 irises in the United Arab Emirates database to analyze the normalization issues raised in different regions of receiver operating characteristic curves.
Daugman
John
J
Computer Laboratory, University of Cambridge, CB3 0FD Cambridge, UK.
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D005544:Forecasting; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007498:Iris / Q000033:anatomy & histology*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000639:trends; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique