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. An estimate of its statistical complexity in a sample of the human population reveals variation corresponding to several hundred independent degrees-of-freedom. Morphogenetic randomness in the texture expressed phenotypically in the iris trabecular meshwork ensures that a test of statistical independence on two coded patterns originating from different eyes is passed almost certainly, whereas the same test is failed almost certainly when the compared codes originate from the same eye. 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 $10^{31}$. The method uses a 256-byte iris code derived from 2-D Gabor filters, which are applied to a doubly dimensionless projected polar coordinate system. This system allows for the representation of iris textures across multiple scales and ensures that the code is invariant to changes in distance, zoom, and eye position. The code is then compared using a statistical test of independence, which is passed almost certainly for codes from different eyes but failed almost certainly for codes from the same eye. The statistical decision theory framework is used to assign confidence levels to recognition decisions, with the goal of maximizing the probability of correct acceptance and rejection while minimizing the likelihood of incorrect decisions. The performance of the system is evaluated using a database of 592 different iris codes, with the results showing that the system has a very low false accept rate, with the probability of two iris codes from different irises agreeing completely by chance being roughly one in $2^{173}$, or approximately $10^{-52}$. The system is robust to variations in iris texture and is capable of recognizing individuals with high confidence.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. An estimate of its statistical complexity in a sample of the human population reveals variation corresponding to several hundred independent degrees-of-freedom. Morphogenetic randomness in the texture expressed phenotypically in the iris trabecular meshwork ensures that a test of statistical independence on two coded patterns originating from different eyes is passed almost certainly, whereas the same test is failed almost certainly when the compared codes originate from the same eye. 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 $10^{31}$. The method uses a 256-byte iris code derived from 2-D Gabor filters, which are applied to a doubly dimensionless projected polar coordinate system. This system allows for the representation of iris textures across multiple scales and ensures that the code is invariant to changes in distance, zoom, and eye position. The code is then compared using a statistical test of independence, which is passed almost certainly for codes from different eyes but failed almost certainly for codes from the same eye. The statistical decision theory framework is used to assign confidence levels to recognition decisions, with the goal of maximizing the probability of correct acceptance and rejection while minimizing the likelihood of incorrect decisions. The performance of the system is evaluated using a database of 592 different iris codes, with the results showing that the system has a very low false accept rate, with the probability of two iris codes from different irises agreeing completely by chance being roughly one in $2^{173}$, or approximately $10^{-52}$. The system is robust to variations in iris texture and is capable of recognizing individuals with high confidence.