An iris-based biometric involves analyzing features found in the colored ring that surrounds the pupil. This uses a fairly conventional camera element and requires no close contact between the user and the reader. Users with glasses with a high dioptre have to take off his/her glasses.
Iris recognition systems have got a very low equal-error-rate and are very secure. The price of an iris-recognition system is very high. An iris-recognition algorithm first has to identify the approximately concentric circular outer boundaries of the iris and the pupil in a photo of an eye. The set of pixels covering only the iris is then transformed into a bit pattern that preserves the information that is essential for a statistically meaningful comparison between two iris images. The mathematical methods used resemble those of modern lossy compression algorithms for photographic images. In the case of Daugman's algorithms, a Gabor wavelet transform is used in order to extract the spatial frequency range that contains a good best signal-to-noise ratio considering the focus quality of available cameras. The result are a set of complex numbers that carry local amplitude and phase information for the iris image. In Daugman's algorithms, all amplitude information is discarded, and the resulting 2048 bits that represent an iris consist only of the complex sign bits of the Gabor-domain representation of the iris image. Discarding the amplitude information ensures that the template remains largely unaffected by changes in illumination and virtually negligibly by iris color, which contributes significantly to the long-term stability of the biometric template.
To authenticate via identification (one-to many template matching) or verification (one-to one template matching) a template created by imaging the iris, is compared to a stored value template in a database. If the Hamming Distance is below the decision threshold, a positive identification has effectively been made. A practical problem of iris recognition is that the iris is usually partially covered by eye lids and eye lashes. In order to reduce the false-reject risk in such cases, additional algorithms are needed to identify the locations of eye lids and eye lashes, and exclude the bits in the resulting code from the comparison operation.