Hindawi Publishing Corporation
EURASIP Journal on Advances in Signal Processing
Volume 2008, Article ID 280635, 10 pages
doi:10.1155/2008/280635

Research Article
A Novel Retinal Identification System
Hadi Farzin,1 Hamid Abrishami-Moghaddam,1 and Mohammad-Shahram Moin2
1 Department of Electrical Engineering, K.N. Toosi University of Technology, Seyed Khandan, 16315-1355 Tehran, Iran2 Iran Telecommunication Research Center, North Kargar, 14399-55471 Tehran, Iran

Correspondence should be addressed to Hamid Abrishami-Moghaddam, moghadam@saba.kntu.ac.ir
Received 1 May 2007; Revised 27 December 2007; Accepted 21 February 2008
Recommended by Nikolaos V. Boulgouris
This paper presents a novel biometric identification system with high performance based on the features obtained from humanretinal images. This system is composed of three principal modules including blood vessel segmentation, feature generation, andfeature matching. Blood vessel segmentation module has the role of extracting blood vessels pattern from retinal images. Feature
generation module includes the following stages. First, the optical disk is found and a circular region of interest (ROI) around it
is selected in the segmented image. Then, using a polar transformation, a rotation invariant template is created from each ROI.
In the next stage, these templates are analyzed in three diﬀerent scales using wavelet transform to separate vessels according totheir diameter sizes. In the last stage, vessels position and orientation in each scale are used to define a feature vector for eachsubject in the database. For feature matching, we introduce a modified correlation measure to obtain a similarity index for eachscale of the feature vector. Then, we compute the total value of the similarity index by summing scale-weighted similarity indices.
Experimental results on a database, including 300 retinal images obtained from 60 subjects, demonstrated an average equal error
rate equal to 1 percent for our identification system.
Copyright © 2008 Hadi Farzin et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1.

INTRODUCTION

Biometric identification systems become a real demand for
improving the security issues in diﬀerent organizations.
Commonly used biometric features include face, fingerprint,
voice, facial thermogram, iris, retina, gait, palm print, handgeometry, and so on [1, 2]. Among these features, retina
may provide higher level of security due to its inherent
robustness against imposture. On the other hand, retinal
pattern of each subject undergoes less modification during
life. In spite of these properties, retina has not been usedfrequently in biometric systems mainly because of technological limitations in manufacturing low-cost scanners [3–6]. This is the reason why few works have been published onhuman identification using retinal images [7–10]. Nowadays,
with the progress in retinal scanner technology, relatively
low-cost retinal scanners are introduced to the market[6, 11]. The first identification system using commercial
retina scanner called EyeDentification 7.5 was proposed by
EyeDentify Company in 1976 [6]. Retinal-based recognition
for personal identification has further desirable properties
such as uniqueness, stability, and noninvasiveness. The

features extracted from retina can identify even among
genetically identical twins [12]. Uniqueness of retina comesfrom uniqueness of blood vessels pattern distribution at the
top of the retina.
Xu et al. [9] used the green grayscale retinal image andobtained vector curve of blood vessel skeleton. Then, they
defined a set of feature vectors for each image including
feature points, directions, and scaling factor. In their method,feature matching consists of finding aﬃne transformation
parameters which relates the query and its best corresponding enrolled image. The major drawback of thisalgorithm is its computational cost, since a number of rigid
motion parameters should be computed for all possible
correspondences between the query and enrolled images inthe database. Xu et al. evaluated their algorithm on a databaseincluding 200 images and obtained zero false recognition
against 38 false rejections. Ortega et al. [10] used a fuzzy
circular Hough transform to localize the optical disk in theretinal image. Then, they defined feature vectors based on
the ridge endings and bifurcations from vessels obtained
from a crease model of the retinal vessels inside the opticaldisk. For matching, they adopted a similar approach as in

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EURASIP Journal on Advances in Signal Processing[9] to compute the parameters of a rigid transformation
between feature vectors which gives the highest matchingscore. This algorithm is computationally more eﬃcient withrespect to the algorithm presented in [9]. However, theperformance of the algorithm has been evaluated using avery small database including only 14 subjects. Recently,
Tabatabaee et al. [8] presented an approach for humanidentification using retinal images. They localized the opticaldisk using Haar wavelet and active contour model and used
it for rotation compensation. Then, they used Fourier-Mellintransform coeﬃcients and complex moment magnitudesof the rotated retinal image for feature definition. Finally,
they applied fuzzy C-means clustering for recognition andevaluated their algorithm on a database including 108 imagesof 27 diﬀerent subjects.
In this paper, we present a new biometric identification
system based on retinal images. The system generates
rotation invariant features by using polar transformation andmultiscale analysis of retinal segmented images. For identification, the system uses a modified correlation function forcomputing similarity index measure. Experimental results
using our new identification system demonstrated its highperformance. Our retinal identification system is novel inthe following ways: (i) our recently introduced state-of-theart algorithm [13] is used for vessel detection; (ii) a new
multiscale code representing the blood vessel distribution
pattern around the optical disk is introduced and usedas feature vector; and (iii) a new similarity index called
modified correlation is defined for feature matching.
This paper is organized as follows. Section 2 will talkabout retinal technology. Section 3 provides an overview
of our new biometric identification system. In Section 4,
we describe our vessel segmentation algorithm. Sections
5 and 6 are devoted to feature generation and matchingmodules, respectively. Evaluation results and discussion arepresented in Section 7. Finally, concluding remarks are given
in Section 8.
2.

OVERVIEW OF RETINAL TECHNOLOGY

2.1. Anatomy of the retina
Figure 1 shows a side view of the eye.The retina is approximately 0.5 mm thick and covers the inner side at the backof the eye [8]. In the center of the retina is the optical nerve
or optical disk (OD), a circular to oval white area measuring
about 2 × 1.5 mm across (about 1/30 of retina diameter) [14].
Blood vessels are continuous patterns with little curvature,
branch from OD and have tree shape on the surface of retina(Figure 2). The mean diameter of the vessels is about 250 μm(1/40 of retina diameter) [14].
The retina is essentially a sensory tissue which consists ofmultiple layers. The retina also consists of literally millions ofphotoreceptors whose function is to gather the light rays thatare sent to it and transform that light into electrical impulses
which travel through the optic nerve into the brain, whichthen converts these impulses into images.The two distinct
types of photoreceptors that exist within the retina are calledrods and cones. The cones (there are about 6 million cones)

Vitreous body
Anterior chamber
Fovea

Lens

Visual axis

Optic nerve

CorneaIris

RetinaScleraChoroid

Figure 1: Eye anatomy [15].

Opticaldisk

Figure 2: Retina images from four diﬀerent subjects.

help us see the diﬀerent colours, and the rods (there are about
125 million rods) help with night and peripheral vision.
2.2.

How the retinal anatomy can be used to
identify people?

When talking about the eye, especially in terms of biometrics,
there is often confusion between the iris and the retina of theeye, in that the two are similar. While the iris and the retina
can be grouped together into one broad category called “eyebiometrics,” the function of the two are completely diﬀerent.
The iris is the colored region between the pupil and thewhite region of the eye (also referred to as the sclera). The
primary role of the iris is to dilate and constrict the size ofthe pupil. As shown in Figure 1, the iris is located in thefront of the eye, and the retina is located towards the backof the eye. Because of its internal location within the eye, theretina is not exposed to the external environment, and thus it
possesses a very stable biometric. It is the blood vessel patternin the retina that forms the foundation for the science andtechnology of retinal recognition. Figure 2 shows diﬀerent
retinas captured from four people.
There are two famous studies which confirmed theuniqueness of the blood vessel pattern of the retina. In 1935,
a paper was published by Simon and Goldstein [7], in which
they discovered that every retina possesses a unique anddiﬀerent blood vessel pattern. They even later published apaper which suggested the use of photographs of these bloodvessel patterns of the retina as a means to identify people.
The second study was conducted in the 1950s by Dr. Paul
Tower. He discovered that even among identical twins, theblood vessel patterns of the retina are unique and diﬀerent[12].

Hadi Farzin et al.

3

2.3. Retinal scanners
The first major vendor for the research/development andproduction of retinal scanning devices was a company called
EyeDentify, Inc. This company was created in 1976. The firsttypes of devices used to obtain images of the retina werecalled “fundus cameras.” These were instruments created forophthalmologists but were adapted to obtain images of theretina. However, there were a number of problems using thistype of device. First, the equipment was considered to be very
expensive and diﬃcult to operate. Second, the light used toilluminate the retina was considered to be far too bright anddiscomforting to the user.
As a result, further research and development were conducted, which subsequently yielded the first true prototype
of a retinal scanning device in 1981. This time, infrared light
was used to illuminate the blood vessel pattern of the retina.
Infrared light has been primarily used in retinal recognition
because the blood vessel pattern in the retina can absorb
infrared light at a much quicker rate than the rest of the tissue
in the eye. The infrared light is reflected back to the retinal
scanning device for processing. This retinal scanning device
utilized a complex system of scanning optics, mirrors, andtargeting systems in order to capture the blood vessel patternof the retina [6]. However, later research and development
created devices with much simpler designs. For example,
these newer devices consisted of integrated retinal scanning
optics, which sharply reduced the costs of production, incomparison to the production costs of the EyeDentification
System 7.5.
The last known retinal scanning device to be manufactured by EyeDentify was the ICAM 2001. This device could
store up to 3000 enrolees, with a storage capacity of up to3300 history transactions [16]. However, this product was
eventually taken oﬀ the market because of user acceptanceand public adoption issues and its high price. It is believedthat some companies like Retica Systems Inc. are working on
a prototype retinal scanning device that will be much easier
to implement into commercial applications and will be much
more user friendly [11].
In summary, given its strong and weak points, retinal
recognition has the potential to be a very powerful biometricidentification technology. In Figure 3, you can see fourtypes of retinal scanners: (a), (b), and (c) correspond tohuman retinal scanner, and (d) corresponds to animal retinal
scanner.
2.4. The applications of retinal recognition
The primary applications for retinal recognition have beenfor physical access entry for high security facilities. This
includes military installations, nuclear facilities, and laboratories. One of the best-documented applications of the useof retinal recognition was conducted by the State of Illinois,in an eﬀort to reduce welfare fraud. The primary purpose
was to identify welfare recipients, so that benefits could not
be claimed more than once. Iris recognition is also usedin conjunction with this project [11]. Retinal imaging is a

(a)

(b)

(c)

(d)

Figure 3: Some retinal scanners, (a) a human retinal scanner, (b)and (c) human retinal recognition scanners, and (d) a cow retinal
scanner.

form of identification that can be used in both animals andhumans.
2.5.

The strengths and weaknesses ofretinal recognition

Retinal recognition also possesses its own set of strengths andweaknesses, just like all other types of biometric technology.
The strengths can be described as follows.
(i) The blood vessel pattern of the retina hardly everchanges over the lifetime of an individual. Moreover,
the retina is not exposed to the threats posed by
the external environment, as other organs such as
fingerprint.
(ii) The retinal recognition is robust against imposture
due to inaccessibility of the retina.
(iii) The actual average feature vector size is very small
compared to other biometric feature vectors. This
could result in quicker verification and identification
processing times, as opposed to larger sized feature
vectors such as in iris recognition systems [17], which
could slow down the processing times.
(iv) The rich and unique features which can be extracted
from the blood vessel pattern of the retina.
The weaknesses can be described as follows.
(i) An individual may be aﬄicted with some diseases ofthe eye such as hard glaucoma, cataracts, and so on
which complicate the identification process.
(ii) The image acquisition involves the cooperation of
the subject, entails contact with the eyepiece, and4

EURASIP Journal on Advances in Signal Processing

Retinalimage

Blood vessels
segmentation

Polar transformation(rotation invariant)Vesselmasking

Wavelettransform

Database
Featuregeneration

Featurematching

Large
vessels(scale 3)

Mediumvessels(scale 2)

Smallvessels(scale 1)

Featureextraction

Featureextraction

Featureextraction

Similarity
index

Similarity
index

Similarity
index

Fusion SIs
ID accept
or rejectThresholding

Figure 4: Overview of the proposed retinal identification system.

requires a conscious eﬀort on the part of the user. All
these factors adversely aﬀect the public acceptabilityof retinal biometric.
(iii) Retinal vasculature can reveal some medical conditions, for example, hypertension which is anotherfactor deterring the public acceptance of retinal scanbased biometrics.
3.

PROPOSED SYSTEM BLOCK DIAGRAM

Figure 4 illustrates diﬀerent parts of our new biometricidentification system based on retinal images. As illustrated
in the block diagram, this system is composed of threeprincipal modules including blood vessel segmentation,
feature generation, and feature matching. Blood vessel segmentation provides a binary image containing blood vessels
pattern which will be used by the next module. Feature
generation module contains several submodules: (i) vessel
masking in the vicinity of OD, (ii) polar transformation to
obtain a rotation invariant binary image containing major
retinal vessels, (iii) multiscale analysis of the resulted binary
image using wavelet transform in order to separate vessels
according to their diameter sizes,and (iv) feature vector
construction from three images, each containing vessels
with specified range of diameter size. Feature matchingmodule contains the following submodules: (i) computationof similarity indices called SIs for three diﬀerent scales,(ii) scale-weighted summation of SIs for generating thetotal SI, and (iii) thresholding the computed SI for subject
identification.
4.

BLOOD VESSEL SEGMENTATION

Blood vessel segmentation is essential for our biometricidentification system. For extracting retinal vessels, we usean algorithm, recently introduced by Farzin et al. [13] basedon a local contrast enhancement process. This algorithm
includes the following steps: (i) using a template matching
technique OD in retinal image is localized; (ii) the original image is divided by the correlation image obtainedin the previous step to achieve a new image in which
undesired brightness eﬀect of OD is suppressed, (iii) the
vessel/background contrast is enhanced using a new local
processing operation based on statistical properties of theresulted image, and (iv) finally, a binary image containing
blood vessels is resulted by histogram thresholding of thecontrast enhanced image.
4.1.

Localizing optical disk and removing
its effect in retinal image

Here, we use a template matching technique to localize theoptic disk. For this purpose, we correlate the original green
plane image with a template. The template is generated byaveraging rectangular ROIs containing OD in our retinal
image database [13]. After correlating each retinal image
with the template, OD is localized as a bright region in thecorrelated image with high density of vessels. Figure 5 shows
the template and the resulted correlated image. As illustrated,the bright region in the correlated image corresponds to OD
in the original image.
The original image is subsequently divided (pixel by
pixel) by the correlation image obtained in the previous stepto achieve a new image in which undesired brightness eﬀectof OD is suppressed (Figure 6).
The location of OD in retinal images varies from one
subject to another due to natural variations in the position
of OD in the retina and also due to gaze angle. This variation
may degrade the recognition performance of the system.
However, since our retinal recognition system is based on

Hadi Farzin et al.

5

(a)

(b)

(c)

Figure 5: Optical disk localization: (a) original image, (b) template, and (c) correlated image.

(a)

(b)

Figure 7: Local contrast enhanced image.
Figure 6: OD removing results: (a) original image, (b) reducing ofOD eﬀect in the original image.

the vessel distribution pattern in the vicinity of OD, its
localization may be used for compensating the variation ofvessel distribution pattern caused by the variation in OD
location.
(a)4.2. Local contrast enhancement
In local processing operation, a sliding window of size M ×
M (M is at least 50 times smaller than the dimensions of
the original image) is used to obtain a contrast enhanced
image. In each pixel, the new value is computed using
the mean/variance of window values and global maximum/minimum values of the pixels in the original image. Letf (i, j) be the value of the pixel (i, j) in the original image.
The enhanced image g(i, j) is computed according to thefollowing equations [13]:f (i, j) −→ g(i, j) =H=

H − wmin,wmax − wmin

wmin =mean√ 
1 + exp − mean − fmin / var ,wmax =

mean
 
√ 
1 + exp − mean − fmax / var ,Figure 8: Morphological correction: (a) vessels after contrast
enhancement, (b) vessels after morphological correction.

where var and mean are variance and mean of the values
inside the window, and fmin and fmax are global minimum
and maximum of the original green plan image, respectively.
It is clear that H is a mapping function from f to g. Figure 7
shows the local contrast enhanced image.
4.3.

1
0.98 √ mean + (1/ var) exp − mean − f (i, j)/ var ,


√



(1)

(b)

Morphological enhancement

After local contrast enhancement process, we encounter aproblem that large vessels are transformed to two parallel
curves as illustrated in Figure 8(a). This problem is causedby small size of the selected window (in the previous step)
compared to the large vessels size. To solve this problem
without modifying vessels thickness, we use morphologicaldilation and erosion to fill the blank space between thetwo parallel curves. Figure 8(b) shows the large vessel in
Figure 8(a) after morphological correction.

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EURASIP Journal on Advances in Signal Processing
5.2.

r2

(a)

r1

(b)

Figure 9: Blood segmentation and masking: (a) vessels pattern, (b)region of interest of vessels images around OD.

Polar transformation and rotation invariancy

Eye and head movements in front of the scanner may resultin some degrees of rotation in retinal images acquired fromthe same subject. Therefore, rotation invariant features are
essential for preventing identification errors caused by image
rotation. This is the reason why we use polar transformation
to obtain a rotation invariant binary image containing
retinal vessels in the vicinity of OD. Polar image can beconstructed by the following transformations from Cartesian
coordinates. The point (x,Cartesian coordinates is y) intransformed to the point ρ = (x2 + y 2 ), θ = arctg(y/x)
in the polar coordinates. A polar image created from ROI
image is shown in Figure 10. The polar image size is 30 × 360
in which the second dimension refers to view angle of ROI.

4.4. Histogram thresholding
To achieve a binary image containing the blood vessels pattern, we apply an optimal thresholding technique [18] to theresults provided by the previous stage. Figure 9(a) illustrates
the final vessel segmentation result after thresholding.
4.5. Segmentation results
The vessel segmentation algorithm was presented and evaluated in detail in our previous paper [13]. This algorithm wasapplied to two databases including DRIVE [19] and STARE[20]. The average accuracies of our algorithm were 0.937
and 0.948 for DRIVE and STARE databases, respectively,
which are comparable to state-of-the-art vessel segmentation
methods [15, 19–25].
5.

FEATURE GENERATION

Our retinal identification system uses features of bloodvessels pattern including their diameters and their relative
locations and angles. For generating these features, thealgorithm uses four submodules as briefly introduced in
Section 2.1. Detailed descriptions of these submodules are
given in the following subsections.
5.1. Vessel masking in the vicinity of OD
Vessels around OD are more important for identification
purposes because their distribution pattern around OD
has less randomness within a subject. In other words, as
the vessels are farther from OD, they become thinner andtheir distribution is more random such that it has lessdiscriminative property. Hence, OD location can be used asa reference point for positioning the human eye with respect
to the scanner system. This means that OD should be placedat the central region of the scanned image in order to allow
the system to perform the identification. After extracting thevessels and localizing OD by vessel segmentation algorithm,
we focus on vessels in the vicinity of OD. A ring mask
centered at OD location, with radii r1 and r2 (r1 < r2), isused to select a ROI in the vessel-segmented binary image(Figure 9(b)). This binary ROI is used for feature generationin the next stages.

5.3.

Multiscale analysis of the polar image

Vessels in the vicinity of OD have diﬀerent ranges of diameter
size. This property may be used as the first feature in thefeature generation module. In this way, one can emulate ahuman observer mental activity in multiscale analysis of thepolar image. In other words, a human observer classifies
vessels in the vicinity of OD into large, medium, and small
sizes, and uses their relative positions for identification ofeach individual. For this purpose, we analyze the polar image
in three scales by means of discrete stationary biorthogonal
wavelet transform. Obviously, alternative methods such as
using image processing for determining vessels diameters canbe used. However, the diameter nonuniformity of each vesselin the polar image may complicate this kind of approaches(see Figure 11(b)). Figure 11(a) shows residual coeﬃcientsresulted from applying wavelet transform to the polar image
in Figure 10(b) in the first three scales. To extract large vessels
from polar image, we threshold residual coeﬃcients in thethird scale of the wavelet transform. For extracting mediumsize vessels, we remove large vessels from the polar image
and repeat the same procedure on residual coeﬃcients of the
wavelet transform in the second scale. Finally, we remove
large- and medium-size vessels from the polar image inorder to obtain small vessels. The result of vessel separation
procedure is illustrated in Figure 11(b).
5.4.

Feature vector construction

Figure 12 illustrates how a feature vector is constructed usinga wavelet decomposed polar image. For constructing thefeature vector, we localize vessels in each scale and replacethem with rectangular pulses. The duration of each pulse
is experimentally fixed to 3 points, and its amplitude isequal to the angle between corresponding vessel orientationand the horizontal axis. Therefore, the final feature vector
is composed of 3 vectors (one per scale), each containing
360 values. Evidently, zero values in each vector correspondto nonvessel positions in the wavelet decomposed polar
image. Further consideration should be given to the memory
size required for each feature vector. One may reduce theredundancy of feature vectors using run length coding(RLC). This coding can reduce the average size of featureHadi Farzin et al.

7

(a)

(b)

Figure 10: Polar transformation: (a) ROI in Cartesian coordinates, (b) polar image.

(a)

(b)

Figure 11: (a) Multiscale analysis of polar image: wavelet approximation coeﬃcients in scale 3 (up), 2 (middle), and 1 (bottom); (b) vessel
separation result: large (up), medium (middle), and small (bottom) vessels.

vectors from 3 × 360 bytes to only 3 × 48 bytes, which issignificantly smaller than 256 bytes for iris code [17].
6.

FEATURE MATCHING

For feature matching, we introduce a new similarity index
based on a modified correlation between the feature vectors.
Modified correlation (MC) between two feature vectors forthe ith scale is defined as follows:
MCi (ϕ) =

N

τ =1



q

step θi (τ) × θi (τ + ϕ)






q
∗cos α × θi (τ) − θi (τ + ϕ) ,i = 1, 2, 3,(2)

where θi is the feature vector corresponding to the enrolledqimage, and θi is the feature vector corresponding to the input
query image, α is a coeﬃcient experimentally set to 1.7, τrepresents the circular translation value, and N = 360 is thelength of the feature vector in each scale. step (·) is the step
function defined as follows:
step (x) =1,0,

x > 0,x ≤ 0.

(3)

The role of step (·) in (2) is to normalize the product of pulse
amplitudes in the feature vectors, because the amplitude ofeach pulse specifies the orientation of the corresponding

vessel and is used only for determining the argument ofcos (·) in (2). The role of cos (·) in (2) is to take into accountthe angle between vessels in the enrolled and query images.
Since the angle between vessels rarely exceeds 90 degrees, weuse a coeﬃcient α(∼=2) in the argument of cos (·) in orderto reduce the modified correlation value when the vessels are
not oriented in the same direction. If the two vessels have thesame orientation, the angle between them will approach tozero and cos (·) will take a value close to 1. In contrary, if they
are oriented diﬀerently (e.g., about 90 degrees), the angle
between them will be diﬀerent from zero and cos (·) willapproach to −1. The similarity index between the enrolled
and the query image corresponding to the ith scale is definedas the maximum value of the modified correlation function:
SIi = Max

ϕ=1,...,360

MCi (ϕ) ,i = 1, 2, 3.

(4)

Finally, a scale-weighted summation of SIs is computed toobtain a total SI for the enrolled and query images. In
general, larger vessels are more eﬀective than smaller ones
for identification. Therefore, we used three diﬀerent weights(w1 > w2 > w3 ) to obtain the weighted sum of similarity
indices as follows:
SI = w1 × SI1 + w2 × SI2 + w3 × SI3 ,(5)

where SI is the total similarity index which is used for identification. In this work, we used the following experimental
weights: w1 = 2.0, w2 = 1.5, and w3 = 0.5.

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EURASIP Journal on Advances in Signal Processing
θ(120)

θ(340)

θ(120)

θ(340)

θ1

ϕ119120121

257258259

339340341

360

Figure 12: Construction of feature vector in the second scale (medium-size vessels), the horizontal axis, shows the position of vessels(in degrees) in polar coordinates and the vertical axis and shows the angle (in degrees) between corresponding vessel orientation and thehorizontal axis in the polar image.
Table 1: Experimental results.
Experiment
Accuracy

Experiment B
97.9%

Experiment C
98.9%

We applied the algorithm on a database including 60
subjects, 40 images from DRIVE [19], and 20 images fromSTARE [20] database. We rotated randomly each image 5times to obtain 300 images. We evaluated the performanceof our identification system in four diﬀerent experiments asfollows.

Experiment D
99.6%

FRR
0.08

FAR

0.07
0.06
0.05
0.04
0.03

The first 30 images of DRIVE database were enrolled, and 60
images of DRIVE and STARE databases with 5 images per
subject were entered to the system as queries.

EER:(0.006, 7.83)

0.02
0.01
0

0

2

4

Experiment B

The first 15 images of DRIVE database and the last 15 imagesof STARE database were enrolled, and 60 images of DRIVE
and STARE databases with 5 images per subject were entered
to the system as queries.
These experiments demonstrated that our system has anaverage accuracy equal to 99.0 percent. Table 1 shows theresults of each experiment. Figure 13 shows the variation of
FRR and FAR according to the distribution of nonmatching
distance by selecting a proper distance threshold. Also, in

8
10Threshold

12

14

16

100
99
Genuine acceptance rate (%)

Experiment D

6

Figure 13: Intersection of FRR and FAR diagram shows EER for
Experiment A with α = 1.7.

Experiment C
The first 10 images of DRIVE database and the first 10
images of STARE database were enrolled, and 60 images from
DRIVE and STARE databases with 5 images per subject wereentered to the system as queries.

Variance
0.58

0.09

Experiment A

The last 30 images of DRIVE database were enrolled, and 60
images of DRIVE and STARE databases with 5 images per
subject were entered to the system as queries.

Mean
99.0%

0.1

EXPERIMENTAL RESULTS

Error rate

7.

Experiment A
99.4%

98
97
96
95
94
93
92
91
90

0

20

40
60
False acceptance rate (%)

Figure 14: ROC curve.

80

100

Hadi Farzin et al.
Figure 14, the ROC curve shows that in a very small false
acceptance rate we have large values of genuine acceptance
rate for identification.
8.

CONCLUSIONS AND PERSPECTIVESIn this paper, a novel biometric system was introduced
using unique features from retinal images. Advantages of thissystem can be summarized as follows.
(i) It needs small memory size, since feature vectors are
fairly compact.
(ii) In the proposed system, the identification result is
not influenced by gaze angle, since the OD location
is used as a reference point for feature extraction, andonly blood vessels pattern around OD are detected
and used for feature generation. Therefore, if OD is
not located in an authorized position around image
center, it can be detected and alarmed to the subject
for a new scan with correct gaze angle.
(iii) Since the vessels pattern only in the vicinity of OD isused for feature generation, the vessel segmentation
may be performed only in the vicinity of OD which
reduces drastically the computational cost of thealgorithm.
(iv) Our feature generation algorithm uses multiscale
analysis of the polar image which in contrary toother image processing techniques is less sensitive tosmall variations of the vessels diameters and extracts
a considerable amount of information.
The primary results obtained by our retinal recognition
system demonstrate its potential for being used as a reliablebiometric system. Further enhancements to our retinal
recognition system can be provided by the following:(i) most of the parameters used in the algorithm havebeen selected experimentally in order to obtain goodresults. These parameters such as the weights used inmatching process can be optimized for providing ahigher average accuracy;(ii) the eﬀect of the optical disk position within theretinal image can be reduced by performing anormalizing transformation which brings OD to thecenter of the retinal image. In this way, the resulted
retina codes will be less sensitive to the OD position
within the retinal image.
ACKNOWLEDGMENT
This work was partially supported by Iran Telecommunication Research Center under Grant no. T-500-7100.
REFERENCES[1] A. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in a Networked Society, Kluwer Academic Publishers,
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