Iris recognition is an automated method of capturing a person’s unique biological data that distinguishes him or her from another individual. It has emerged as one of the most powerful and accurate identification techniques in the modern world. It has proven to be most fool proof technique for the identification of individuals without the use of cards, PINs and passwords. It facilitates automatic identification where by electronic transactions or access to places, information or accounts are made easier, quicker and more secure.
A method for rapid visual recognition of personal identity is described, based on the failure of 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 trabeclar meshwork ensures that a test of statistical independence on two coded patterns organizing 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 512 – byte “IRIS–CODE” statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris code at the rate of 4,000 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 1,31,000 when a decision criterion is adopted that would equalize the False Accept and False Reject error rates.Recommend this topic