**3. Biometric recognition and verification**

A biometric recognition system (BRS) recognizes a person by analyzing the random pattern in his/her physiological or behavioral characteristics known as biometrics, which are unique, non-transferable, unforgettable, and always carriable. Among biometric characteristics, we can also count voice, signature, face, gait, fingerprint, DNA, odor, vein pattern, hand geometry, signature, iris, retina, etc. A BRS has become a usual requirement in strictly protected areas such as nuclear plants, military facilities, scientific laboratories, cash vault, border, airport, government office etc., as well as in our day-to-day life such as online banking, car locking, building access, phone unlocking etc. Comparing to other biometrics, eye biometrics, which include iris and retina [8], offers higher degree of randomness. Even for identical twins the pattern of retinal blood vessels and iris are very distinctive [14, 15]. In addition, eye biometrics remain the same for the entire lifetime of a person. Therefore, the error rate of eye biometrics-based BRS is very low. Even though, both eye biometrics offer very high security, the probability of occurrence of counterfeiting is lower in the retina-based BRS (RBRS) than the iris based BRS. Because without users' cooperation and special camera like fundus camera or ophthalmoscope, it is not possible to capture retinal image. On the other hand, iris images can be captured by a normal camera at a distance.

In an RBRS, the unique pattern of blood vessels in the retinal image is used to recognize a person. Four kinds of approaches are generally found in the literature for capturing the uniqueness of retinal vessels, among which one approach is matching

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*Retina Recognition Using Crossings and Bifurcations DOI: http://dx.doi.org/10.5772/intechopen.96142*

bifurcations and crossings of the blood vessel structure [16]. Bifurcation is the point where one blood vessel divides into two branches. The crossing is the point where blood vessels cross one another. Inspired by the idea of fingerprint minutiae [17, 18], Ortega et al., claimed in [9, 13] that using of bifurcations and crossings as feature points can overcome the drawback of the RBRS which uses the tree shape pattern of

The retina is suitable for biometric purposes. As already mentioned, the pattern of blood vessels is unchanged during human life. In addition, the retina is well protected from the environment. However, this is also a disadvantage, because its capture is relatively complicated. In order to uniquely identify a person by capturing the uniqueness of the eye's tree-shaped blood vessels, four kinds of approaches have been proposed [16]: (i) using general signal and image processing techniques on the raw retinal images; (ii) matching of the branching blood vessel structure as a whole; (iii) matching bifurcations and crossings of the blood vessel structure; and (iv) matching the pattern of the vessels that are traversing a well-defined region. In all these approaches, a database is created by storing the templates made by the features in the training phase. These features are accessed in the identification phase. The uniqueness or randomness of tree-shaped blood vessels can be measured by *biometric entropy* which has unit in bits. The bigger the biometric entropy, the lower the chance that two retinas of two different persons will match. There are two ways to determine biometric entropy [16]. The first one is to fit the distribution of the features to existing theoretical models and the second one is to determine empirically the probability *p* of matching templates of two different persons. In the second way, the entropy is *log*2 *p*. Arkala et al., [16] measured the biometric entropy in the ring around the blind spot. Each vessel segment was represented by a triplet: *position* (in degrees around the ring), *width* (thickness in degrees), and angle (the angle that the segment makes with a radial line from the ring passing through the segment's centroid). The biometric entropy result was approximately 17 bits. That

The image of the bloodstream in the retina was found to be unique for two individuals. This allows the discovery of eye diseases by ophthalmologists *Carleton Simon* and *Isidore Goldstein* in 1935. Later on, they released a journal article about identifying unique patterns in the retina using vein imaging [20]. Dr. Paul Tower supported it, and in 1955, released an article on the study of monozygotic twins [14]. The article states that the patterns of the retinal vessel have the least resemblance to all the other patterns that were examined. Identifying the retina of the vessels was

Robert Hill, the founder of EyeDentify in 1975, spent most of his time and effort in the development of a simple and fully automated device that can capture a snapshot of the retina and use it to verify the user's identity. These devices, however,

The remaining fundus cameras were modified by several other companies to capture an image of the retina to be used for identification. They, however, had many great disadvantages, such as the corresponding complex alignment of the optical axis, the visible light spectra, which causes discomfort, and the cameras

Infrared (IR) illumination was later discovered and used. Choroid reflects the radiation coming from the almost transparent beams that are hitting it. This reflection creates an image of the blood vessels in the eye. Since it is not visible, the pupil

did not emerge in the market even after several years [8, 21].

diameter is not reduced even when the eye is being irradiated.

blood vessels of the whole retina proposed by Mariño et al. [19].

means 1017 possible combinations of retinal patterns.

**3.1 History of retinal recognition**

an unchanging idea back then.

being too expensive.

#### *Retina Recognition Using Crossings and Bifurcations DOI: http://dx.doi.org/10.5772/intechopen.96142*

*Applications of Pattern Recognition*

**Figure 4.**

will alter the normal sight of the subject. Mydriasis, on the other hand, is required for some subjects. The costs of these devices are calculated by medically specialized

fundus cameras are complex devices which are also quite expensive.

coming from the cornea, which would be useless in raster scanning [13].

A biometric recognition system (BRS) recognizes a person by analyzing the random pattern in his/her physiological or behavioral characteristics known as biometrics, which are unique, non-transferable, unforgettable, and always carriable. Among biometric characteristics, we can also count voice, signature, face, gait, fingerprint, DNA, odor, vein pattern, hand geometry, signature, iris, retina, etc. A BRS has become a usual requirement in strictly protected areas such as nuclear plants, military facilities, scientific laboratories, cash vault, border, airport, government office etc., as well as in our day-to-day life such as online banking, car locking, building access, phone unlocking etc. Comparing to other biometrics, eye biometrics, which include iris and retina [8], offers higher degree of randomness. Even for identical twins the pattern of retinal blood vessels and iris are very distinctive [14, 15]. In addition, eye biometrics remain the same for the entire lifetime of a person. Therefore, the error rate of eye biometrics-based BRS is very low. Even though, both eye biometrics offer very high security, the probability of occurrence of counterfeiting is lower in the retina-based BRS (RBRS) than the iris based BRS. Because without users' cooperation and special camera like fundus camera or ophthalmoscope, it is not possible to capture retinal image. On the other hand, iris

In an RBRS, the unique pattern of blood vessels in the retinal image is used to recognize a person. Four kinds of approaches are generally found in the literature for capturing the uniqueness of retinal vessels, among which one approach is matching

The optical device has a complex mechanical construction. The scanning device works based on the concept of medical eye-optic devices. These retinoscopes or

The reflection of a part of the light that came from a beam and hit the retina is scanned by the CCD camera. This concept is similar to retinoscope, where the eye lens concentrates on the retina's surface due to the adjustment made to the beam of light that is coming from it. The ophthalmic lens receives back the reflection of only a part of the transmitted light beam and readjusts it. The beam leaves the eye below the angle where it entered the eye (return reflection). An image showing the eye's surface can be obtained at roughly 10° surrounding the visual axis, as in **Figure 4**. A circular snapshot of the retina is captured by the device from the reflection of light

workplaces and are in tens of thousands of euros.

*Principle for obtaining an eye background image [12].*

**3. Biometric recognition and verification**

images can be captured by a normal camera at a distance.

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bifurcations and crossings of the blood vessel structure [16]. Bifurcation is the point where one blood vessel divides into two branches. The crossing is the point where blood vessels cross one another. Inspired by the idea of fingerprint minutiae [17, 18], Ortega et al., claimed in [9, 13] that using of bifurcations and crossings as feature points can overcome the drawback of the RBRS which uses the tree shape pattern of blood vessels of the whole retina proposed by Mariño et al. [19].

The retina is suitable for biometric purposes. As already mentioned, the pattern of blood vessels is unchanged during human life. In addition, the retina is well protected from the environment. However, this is also a disadvantage, because its capture is relatively complicated. In order to uniquely identify a person by capturing the uniqueness of the eye's tree-shaped blood vessels, four kinds of approaches have been proposed [16]: (i) using general signal and image processing techniques on the raw retinal images; (ii) matching of the branching blood vessel structure as a whole; (iii) matching bifurcations and crossings of the blood vessel structure; and (iv) matching the pattern of the vessels that are traversing a well-defined region. In all these approaches, a database is created by storing the templates made by the features in the training phase. These features are accessed in the identification phase.

The uniqueness or randomness of tree-shaped blood vessels can be measured by *biometric entropy* which has unit in bits. The bigger the biometric entropy, the lower the chance that two retinas of two different persons will match. There are two ways to determine biometric entropy [16]. The first one is to fit the distribution of the features to existing theoretical models and the second one is to determine empirically the probability *p* of matching templates of two different persons. In the second way, the entropy is *log*2 *p*. Arkala et al., [16] measured the biometric entropy in the ring around the blind spot. Each vessel segment was represented by a triplet: *position* (in degrees around the ring), *width* (thickness in degrees), and angle (the angle that the segment makes with a radial line from the ring passing through the segment's centroid). The biometric entropy result was approximately 17 bits. That means 1017 possible combinations of retinal patterns.

### **3.1 History of retinal recognition**

The image of the bloodstream in the retina was found to be unique for two individuals. This allows the discovery of eye diseases by ophthalmologists *Carleton Simon* and *Isidore Goldstein* in 1935. Later on, they released a journal article about identifying unique patterns in the retina using vein imaging [20]. Dr. Paul Tower supported it, and in 1955, released an article on the study of monozygotic twins [14]. The article states that the patterns of the retinal vessel have the least resemblance to all the other patterns that were examined. Identifying the retina of the vessels was an unchanging idea back then.

Robert Hill, the founder of EyeDentify in 1975, spent most of his time and effort in the development of a simple and fully automated device that can capture a snapshot of the retina and use it to verify the user's identity. These devices, however, did not emerge in the market even after several years [8, 21].

The remaining fundus cameras were modified by several other companies to capture an image of the retina to be used for identification. They, however, had many great disadvantages, such as the corresponding complex alignment of the optical axis, the visible light spectra, which causes discomfort, and the cameras being too expensive.

Infrared (IR) illumination was later discovered and used. Choroid reflects the radiation coming from the almost transparent beams that are hitting it. This reflection creates an image of the blood vessels in the eye. Since it is not visible, the pupil diameter is not reduced even when the eye is being irradiated.

The first prototype of the IR device was released in 1981. It has an eye-optic camera to illuminate the IR radiation. The camera was attached to an ordinary personal computer that will be used to analyze the captured image using a simple correlation comparison algorithm.

*EyeDentification System 7.5* was launched after four years by EyeDentify Inc. Its verification is done using the retina image and the PIN entered by the user, with the user data stored in the database [8, 21].

*ICAM 2001* was the last known retinal scanning device that was made by EyeDentify Inc. The device might have been able to store a maximum of 3,000 subjects with a storage capacity of 3,300 history transactions [8]. Unfortunately, the product was withdrawn due to low user acceptance and high price. Other companies, such as Retica Systems Inc., worked on a retinal acquisition device prototype for biometric purposes that might have been more user friendly and easier to integrate into commercial applications. Unfortunately, this device was also a failure in the market.

#### **3.2 Limitations**

Retinal biometrics limitations discourage further use of it as a biometric system. There are still no acceptable solutions found for these shortcomings [21].

*Fear of eye damage -* due to a myth about the devices damaging the retina. The level of infrared illumination used by these devices is low and has proven to be completely harmless. The people must be shared with this information so that they will not be afraid of using these devices.

*Outdoor and indoor use -* the return beam of the light passing through the pupil twice (once inward then outward of the eye) can be greatly weakened if the subject's pupil is too small. This can result to an increase in the false rejection rate.

*Ergonomics -* the subject must be near to the sensor, which may cause discomfort. *Severe astigmatism -* the eye must be focused on a point. This may be difficult for those with visual impairments such as astigmatism, which can negatively affect the template generation.

*High Price -* the cost of optical devices is always more than the price of other biometric systems such as fingerprint or voice recognition capturing devices.

High-security areas such as nuclear and arms development, even manufacturing, government and military facilities, and other critical infrastructure can make use of retinal recognition.

#### **3.3 Recognition schemes**

Several schemes can be used for recognizing retinal images. For instance, a retina image biometric recognition has different approaches. Farzin [8] and Hill [21] have segmented the blood vessels to generate features and store, at maximum, 256 12-bit samples, which are then shrunk to a reference record containing 40 bytes for each of the eye. The Time-domain stores the contrast information. Fuhrmann and Uhl [22] extracted the vessels which obtained the retina code. The retina code is a binary code describing the vessels surrounding the optical disc.

#### **3.4 Verification phase**

In order to be able to use the proposed algorithm universally, and therefore also for the verification phase, it is necessary to choose the parameters with regard to the verification steps. During the verification phase, when recognizing samples that should be identical, we encounter the problem of inaccuracy in imaging. We

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*Retina Recognition Using Crossings and Bifurcations DOI: http://dx.doi.org/10.5772/intechopen.96142*

ignored to a small extent.

**4. Our recognition method**

chamfers).

manual search.

the GDPR Directive [9, 23].

tion when comparing two retinas.

**4.2 Coordinate system**

practically never manage to take a picture in exactly the same way. There are small inaccuracies such as rotating or zooming on the image. These deviations must be

Another problem may be the absence of some points. Even these inaccuracies can affect the similarity score obtained. In the verification phase, it must be with a relatively low penalty. The values in the previous chapter are therefore chosen so that the same algorithm can be used for both the recognition and verification phases.

The distribution of vascular lines in the retina of the human eye is unique (as shown in Chapter 3.1), which is similar to the papillary line on the human fingers. Currently, there is no single approach to retinal recognition. Our procedure follows dactyloscopy, where bifurcations, terminations, positions and directions of a detected point are stored. We look for "anomalies" on the vessels in the retina - the places of visual crossings and bifurcations - and also record their position within the retina. For images, it is not easy to recognize whether it is a crossing or bifurcation of a vessel as the two phenomena often overlap. Therefore, we are only interested in the feature and not on its specific type. The termination of the vessel takes place "until lost" so a specific place cannot and will not be detected. We locate the points according to the position relative to the optical disk and the fovea. Therefore, we also store their position within the image as will be further described in Chapter 4.2 - the coordinate system. The result is a set of vectors such that the system is not affected from the changes in retinal scanning (different rotations, zooms, or

Recognition becomes problematic in the presence of diseases that are manifested by a change in the retina such as bleeding. As with other biometric features, a relatively large amount of human health information can be read from retinal manifestations. Therefore, it is appropriate that the biometric facility manager guarantees the non-misuse or non-storage of this sensitive data, for example under

If we take a brief look at a few images of the human eye retina, we discover that crosses and bifurcations are not equally frequented in various areas. The probability of their occurrence is in some areas higher, in others almost zero. In the beginning, it should be noted that the ability to mark intersections and bifurcations strongly depends on the quality and contrast of the image. In the statistically empty parts are the very small capillaries that are undetectable in the image using automatic or

When we create the frequency map, the points can be regarded with different weights to recognize the pattern. Finding matching points in two retinas being compared in rare occurring sites may score higher than matching points in other areas. Therefore, we tried to statistically evaluate several hundred retinal images and create our own frequency scheme, which we will later use to adjust the evalua-

In order to be able to work uniformly with all retinas without major complications, we have introduced a polar coordinate system, where two values can be used

**4.1 Statistical evaluation of the crossings and bifurcations frequency**

practically never manage to take a picture in exactly the same way. There are small inaccuracies such as rotating or zooming on the image. These deviations must be ignored to a small extent.

Another problem may be the absence of some points. Even these inaccuracies can affect the similarity score obtained. In the verification phase, it must be with a relatively low penalty. The values in the previous chapter are therefore chosen so that the same algorithm can be used for both the recognition and verification phases.
