**4.2. Reliability**

## *4.2.1. Iris*

When scanning the image of an iris, it is possible to obtain insufficient eye information due to ambient light, eyelids being too closed, and so on. However, this is a fairly reliable identification method.

The accuracy of the comparison of the two iris patterns is represented by the so-called Hamming distance, that is, the number of bits in which the comparison of two different iris patterns differs. It is reported that for the probability of an incorrect comparison of 1:26,000,000, the Hamming distance is 0.32 (i.e., only about one-third of the identical bits of the two patterns).

According to EyeDentify, the frequency distribution of the image of each eye compared to any other one approached a very ideal Gaussian curve with a mean value of 0.144 and a standard deviation of 0.176. The corresponding probability of this distribution with a given mean value

Recognition of Eye Characteristics

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http://dx.doi.org/10.5772/intechopen.76026

The retinal identification method is prone to some conditions that need to be met during scanning. Conditions that might raise the false reject rate are, for example, incorrect distance between sensor and eye, dirty optics, contact lens edges, and glasses. Also, ambient lighting results in the subconscious narrowing of the pupil, so sometimes the device cannot be oper-

There are several possibilities on how to test the liveness (anti-spoofing) of the iris. The most common is the iris reaction to a change in light when the pupil diminishes with more intense lighting. This reflex is subconscious, and responses are usually within the range of 250–400 ms. The pupil stretches and expands even under a constant illumination, and this

Another way of anti-spoofing can be eye movement, or blinking by the command of a scan-

and a standard deviation of 0.7 is about one million [26].

**Figure 21.** Hamming distance distribution [26].

ated well in outdoor conditions during daylight hours.

periodic phenomenon is called the *hippus* [28].

**4.3. Anti-spoofing**

*4.3.1. Iris*

ning device.

**Figure 21** shows the distribution of Hamming's distance when comparing the high number of irises [26]. The graph is a binomial distribution with a probability of 0.5. It also follows from the graph that it is highly unlikely that two different irises differ in less than one-third of the information.

## *4.2.2. Retina*

Regarding retinal scanning, its reliability is high. However, there are conditions where it is not possible to obtain a sufficiently good image of the retina. In particular, it is bad illumination—the user has a heavily closed pupil when scanning due to the large amount of light. Another problem occurs with the abovementioned diseases or other dysfunctions of the eye.

Recognition by the retina is not very widespread, perhaps because there are not really many objective tests of this method. In 1991, the international company *Sandia National Laboratory* tested EyeDentify Inc. on several hundred volunteers. The result was a zero false accept rate and false reject rate less than 1% [27]. However, at that time, the testing of biometric systems was in its early stages, so we cannot be sure of the objectivity of the test.

**Figure 21.** Hamming distance distribution [26].

According to EyeDentify, the frequency distribution of the image of each eye compared to any other one approached a very ideal Gaussian curve with a mean value of 0.144 and a standard deviation of 0.176. The corresponding probability of this distribution with a given mean value and a standard deviation of 0.7 is about one million [26].

The retinal identification method is prone to some conditions that need to be met during scanning. Conditions that might raise the false reject rate are, for example, incorrect distance between sensor and eye, dirty optics, contact lens edges, and glasses. Also, ambient lighting results in the subconscious narrowing of the pupil, so sometimes the device cannot be operated well in outdoor conditions during daylight hours.
