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<p>A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code". Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10/sup 31/.</p>
personal identity recognition; statistical independence test; rapid visual recognition; face recognition; phenotypic feature; multiscale quadrature 2-D Gabor wavelet coefficients; iris code; statistical decision theory; exclusive-OR; false accept error rates; false reject error rates; decision confidence levels; probability; decision theory; face recognition; feature extraction; image coding; image texture; statistical analysis

J. Daugman, "High Confidence Visual Recognition of Persons by a Test of Statistical Independence," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 1148-1161, 1993.
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