How does the eye detector work?

Answer 1

Retinal scan is a biometric technique that uses the unique patterns on a person's retina to identify them.

The human retina is stable from birth to death, making it the most accurate biometric to measure. It has been possible to take a retina scan since the 1930s, when research suggested that each individual had unique retina patterns. The research was validated and we know that the blood vessels at the back of the eye have a unique pattern, from eye to eye and person to person. A retinal scan involves the use of a low-intensity light source and coupler that are used to read the blood vessel patterns, producing very accurate biometric data. It has the highest crossover accuracy of any of the biometric collectors, estimated to be in the order of 1:10,000,000.

Development of the technology has taken longer than expected and for many years the process of taking a retinal scan was measured in tens of seconds. New technology is capable of capturing a retinal scan in less than 2 seconds.

Some biometric identifiers, like fingerprints, can be fooled. This is not the case with a retina scan. The retina of a deceased person quickly decays and cannot be used to deceive a retinal scan. It is for this reason that retina scan technology is used for high end access control security applications.

Iris recognition

Iris recognition is a method of biometric authentication that uses pattern recognition techniques based on high-resolution images of the irides of an individual's eyes. Not to be confused with another less prevalent ocular-based technology, retina scanning, iris recognition uses camera technology, and subtle IR illumination to reduce specular reflection from the convex cornea to create images of the detail-rich, intricate structures of the iris. These unique structures converted into digital templates, provide mathematical representations of the iris that yield unambiguous positive identification of an individual.

Iris recognition efficacy is rarely impeded by glasses or contact lenses. Iris technology has the smallest outlier (those who cannot use/enroll) group of all biometric technologies. The only biometric authentication technology designed for use in a one-to many search environment, a key advantage of iris recognition is its stablity, or template longevity as, barring trauma, a single enrollment can last a lifetime.

Breakthrough work to create the iris recognition algorithms required for image acquisition and one-to-many matching was pioneered by John G. Daugman, Ph.D, OBE (University of Cambridge Computer Laboratory), who holds key patents on the method. These were utilized to effectively debut commercialization of the technology in conjunction with an early version of the IrisAccess system designed and manufactured by Korea's LG Electronics. Daugman's algorithms are the basis of almost all currently (as of 2006) commercially deployed iris-recognition systems. It has a so far unmatched practical false-accept rate of zero; that is there is no known pair of images of two different irises that the Daughman algorithm in its deployed configuration mistakenly identifies as the same. (In tests where the matching thresholds are – for better comparability – changed from their default settings to allow a false-accept rate in the region of 10−3 to 10−4. The IrisCode false-reject rates are comparable to the most accurate single-finger fingerprint matchers

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.

Answer 2

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