Chapter 3
Iris Images Databases and Image
Acquisition Framework
With the pronounced need for reliable personal identification, iris recognition has become an
important enabling technology in the society. Although an iris pattern is a naturally ideal
identifier, the development of a high-performance iris recognition algorithm and transferring
it from research lab to practical applications is still a challenging task. Automatic iris
recognition has to face unpredictable variations of iris images in real-world applications. For
example, recognition of iris images of poor quality, nonlinearly deformed iris images, iris
images at a distance, iris images on the move, and faked iris images all are open problems in
iris recognition. A basic work to solve the problems is to design and develop a high quality
iris image database including all these variations. Moreover, databases of iris images created
by various research groups help us to identify some frontier problems in iris recognition and
leads to improvement in iris recognition technology.
Currently deployed systems rely on good quality images, captured in a stop-and-stare
interface, at close distances and using near infrared (700-900 nm) wavelengths. A study
conducted by Aton Origin for the United Kingdom Passport Service [60] the report; these
imaging constraints are a major obstacle for the massification of iris-based biometric
systems. As compared to other traits, the iris scored relatively low, due to excessive time and
effort demanded from subjects in the data acquisition process. Further advances in iris
recognition technologies are needed to meet the full range of operational requirements,
which essentially focus in the handling of non-ideal biometric samples.
In 2004, Soft Computing and Image Analysis Group (SOCIA Lab.), Department of
Computer Science, University of Beira Interior, Covilhã, Portugal released the UBIRIS
database [54]. The purpose was to simulate less constrained imaging processes and acquire
29
visible wavelength images with several types of data occluding the iris rings (considered
noise). A large number of experiments were conducted on this database and reported in the
literature, although the realism of its noise factors received some criticisms. This was a major
motivation for the development of a new version of the database (UBIRIS.v2, NICE I, NICE
II) in which the images were actually captured on non-constrained conditions (at-a-distance,
on-the-move and in the visible wavelength), with corresponding more realistic noise factors.
3.1
Iris Image Databases in Public Domain
The following iris images databases are free available for research purpose.
CASIA Database: This database is created by the Centre for Biometrics and Security
Research (CBSR) at Institute of Automation, Chinese Academy of Sciences (CASIA),
Beijing, China [22], [58].
CASIA Iris Image Database Version 1.0 (CASIA-IrisV1) includes 756 iris images from 108
eyes. For each eye, 7 images are captured in two sessions whose images are stored as BMP
format with a resolution of 320×280. In this iris database, the original pupil region of the iris
image is edited by CASIA so that the pupil region has a constant “dark “intensity value. This
kind of image retouching may have an impact on the accuracy of the performance
evaluation.
CASIA-IrisV2 consists of total 1200 images of 60 unique subjects and images are stored as
BMP format with a resolution of 640×480.
CASIA-IrisV3 includes three subsets which are labelled as CASIA-Iris-Interval, CASIA-IrisLamp, and CASIA-Iris-Twins. CASIA-IrisV3 contains a total of 22,034 iris images from
more than 700 subjects. All iris images are 8 bit gray-level JPEG files, collected under near
infrared illumination. Almost all subjects are Chinese except a few in CASIA-Iris-Intervals.
Because the three data sets were collected in different times, only CASIA-Iris-Interval and
CASIA-Iris-Lamp have a small overlap in subjects.
Quality of images present in the
database also varies from high-quality images with extremely clear iris textural details to
images with nonlinear deformation due to variations in visible illumination and it contains
original unmasked images [58].
30
CASIA-Iris-Interval: Iris images of CASIA-Iris-Interval were captured with homemade
close-up iris camera. The most compelling feature of this iris camera is that it can capture
very clear iris images due to its circular NIR LED array, with suitable luminous flux for iris
imaging.
CASIA-Iris-Lamp: Iris images of CASIA-Iris-Lamp database were captured using a handheld iris sensor produced by OKI. A lamp was turned on/off close to the subject to
introduce more intra-class variations. Elastic deformation of iris texture due to pupil
expansion and contraction under different illumination conditions is one of the most
common and challenging issues in iris recognition. So CASIA-Iris-Lamp is good for
studying problems of non-linear iris normalisation and robust iris feature representation.
CASIA-Iris-Twins: CASIA-Iris-Twins contains iris images of 100 pairs of twins,
which were collected during Annual Twins Festival in Beijing using OKI's IRISPASS-h
camera. Although iris is usually regarded as a kind of phenotypic biometric
characteristics and even twins have their unique iris patterns, it is interesting to study the
dissimilarity and similarity between iris images of twins.
UPOL Database: The UPOL iris image database was built within the University of
Palack´eho and Olomouc [52]. Its images have been captured through an optometric
framework (TOPCON TRC50IA) optical device connected with SONY DXC-950P 3CCD
camera; the acquired images are of extremely high quality and suitable for the evaluation of
iris recognition in completely noise-free environments. The database contains 384 images
extracted from both eyes of 64 subjects (three images per eye). The images are: 24 bit RGB, 576 x 768 pixels, file format: PNG
BATH Database: This database is created by the researchers in Biometric Signal Processing
group of Department of Electronics and Electrical Engineering, University of Bath, UK.
BATH iris database contains 1000 iris images from 50 eyes with 20 images taken from each
eye. All images are of size 1280 x 960 and compressed using the JPEG2000 codec. In
conjunction with the University of Bath, Smart Sensors Limited has collected a significant
database of high quality (1280 x 960 pixel resolution) iris images for use in research and
31
evaluation. Currently the full database comprises 800 people (1600 eyes) with 20 images of
each left and right eye [87].
UBIRIS Database: This database is prepared and developed by Soft Computing and Image
Analysis Group (SOCIA Lab.), Department of Computer Science, University of Beira
Interior, Covilhã, Portugal [54].
UBIRIS.v1 database is composed of 1877 images collected from 241 persons during
September, 2004 in two distinct sessions. Its most relevant characteristic is incorporation of
images with several noise factors, simulating less constrained image acquisition
environments. This enables evaluation of the robustness of iris recognition methods. For the
first image capture session, the enrolment one, they tried to minimize noise factors, specially
those relative to reflections, luminosity and contrast, having installed image capture
framework inside a dark room. In the second session, they changed the capture place in order
to introduce natural luminosity factor. This propitiates the appearance of heterogeneous
images with respect to reflections, contrast, luminosity and focus problems. Images collected
at this stage simulate the ones captured by a vision system without or with minimal active
participation from the subjects, adding several noise problems.
The purpose of the UBIRIS.v2 database was to constitute a new tool to evaluate the
feasibility of visible wavelength iris recognition under far from ideal imaging conditions. In
this scope, the various types of non-ideal images, imaging distances, subject perspectives and
lighting conditions existent on this database could be of strong utility in the specification of
the visible wavelength iris recognition feasibility and constraints.
NICE.I and NICE.II: Iris database contains total 3000 images in test and training folders
[55], [57]. The imaging framework used in the acquisition of the UBIRIS.v2 data set was
installed in a lounge under both natural and artificial lighting sources. They placed several
marks on the floor (between three and ten meters away from the acquisition device) and
performed two distinct acquisition sessions, each lasting two weeks and separated by an
interval of one week. From the first to second session, the location and orientation of the
acquisition device and artificial light sources was changed. A large majority of the volunteers
were Latin Caucasian (approximately 90%), but they also included black (8%) and Asian
32
people (2%). Approximately 60% of the volunteers participated in both imaging sessions,
whereas 40% participated exclusively in one or the other. Subjects were asked to walk at a
slightly slower than normal speed and to look at several lateral marks that obliged them to
rotate their head and eyes, enabling the manual capture of three images per meter, between
eight and four meters, giving a total of 15 images per eye and session. It should be stressed
that they requested this cooperative behaviour, for the unique purpose of maximizing the
number of usable images per subject and imaging session. A completely covert procedure
could have been used, with a necessarily lower number of usable images per session.
MMU Database: This database is created by research group at Multimedia University,
Malaysia.
MMU.1 iris database contributes a total number of 450 iris images which were taken using
LG IrisAccess®2200. This camera is semi-automated and it operates at the range of 7-25 cm.
On the other hand, MMU.2 iris database consists of 995 iris images. The iris images are
collected using Panasonic BM-ET100US Authenticam and its operating range is even farther
with a distance of 47-53 cm away from the user. These iris images are contributed by 100
volunteers with different age and nationality. They come from Asia, Middle East, Africa and
Europe. Each of them contributes 5 iris images for each eye. MMU.1 iris database
contributes a total number of 450 iris images and MMU.2 iris database consists of 995 iris
images which are stored in BMP format with resolution 320×240 [53].
WVU Database: This database is created by the research group at West Virginia University
USA under National Science Foundation research grant.
This database comprised of 1852 images from 380 different eyes. Iris images of the WVU
database were captured with less constrained imaging conditions and due to this, incorporate
several types of noises such as iris obstructions, poor focused and off-angle iris images [86].
ND Iris 2004 – 2005 Database: This database is created by the Computer Vision Research
Lab (CVRL) at University of Notre Dame, USA.
33
The research group of Computer Vision Research Lab (CVRL) at the University of Notre,
USA began collecting iris images in the spring semester of 2004. The initial data collections
used an LG 2200 iris imaging system for image acquisition. Image datasets acquired in
2004-2005 at Notre Dame with this LG 2200 have been used in the ICE 2005 and ICE 2006
iris biometric evaluations. This dataset is a superset of the iris image datasets used in ICE
2005 and ICE 2006.
The ND 2004-2005 iris image dataset contains 64,980 images corresponding to 356 unique
subjects. We have taken first 5 images of 356 subjects. The age group of the subjects is 18 to
75 years. 158 of the subjects are female, and 198 are male. 250 of the subjects are Caucasian,
82 are Asian, and 24 are other ethnicities. None of the images correspond to subjects wearing
glasses during image acquisition. However, a significant fraction of the subjects wore contact
lenses [88].
3.2
Iris Image Acquisition Framework
We have created and developed iris images database for biometric research with emphasis
on Indian subjects for dataset collection.
The iris image acquisition (since April 2010) is carried out by using the following Iris Image
Acquisition systems:
1) I - Scan2 from Crossmatch Technologies
2) Mobile Eyes from L1-Identity Solutions
The specifications of I - Scan2 from Crossmatch Technologies are as follows:

Iris Scans: Dual optical system

Biometric Data Interchange Formats: ANSI INCITS 379-2004; ISO/IEC 19794-6

Operating Temperature: 32°F to 120°F (0°C to 49°C)

Humidity Range: 10-90% non-condensing

Weight: 1.1 lbs (0.5 kg)

Interface: USB 2.0 (no external power)

Auto Capture: Yes

Dimensions: 5.8 x 15.24 x 15.24 cm

Non-contact capture: 12cm
34

Capture rate: 15, 30 FPS

Illumination: Near Infra-Red

Operating system: Windows XP Professional

Hardware: 1.3 GHz or higher Pentium 4 or Compatible CPU
The typical iris image acquisition using I - Scan2 device is shown in Figure 3.1.
Figure 3.1 Iris image acquisition set up using I - Scan2
The sample images acquired using above device are shown in Figure 3.2
(a)
(b)
Figure 3.2 Examples of iris images (a) Iris image 1_S1_R_3
35
(b) Iris image 1_S1_L_3
The specifications of Mobile Eyes from L1 Identity Solution are as follows:

Iris Scans: Dual optical system

Biometric Data Interchange Formats: ISO JTC1 Sc 37 1.37.19794.

Operating Temperature: 32°F to 120°F (0°C to 49°C)

Humidity Range: 10-90% non-condensing

Weight: 2.2 lbs (1.0 kg)

Interface: USB 2.0 (no external power)

Auto Capture: Yes

Dimensions: 17.5 x 7.1 x 20.6 cm

Non-contact capture: 5.8 cm

Capture rate: 30 FPS

Illumination: Near Infra-Red

Operating system: Windows XP Professional, Windows 7 Professional

Hardware: 1.3 GHz or higher Pentium 4 or Compatible CPU
The typical iris image acquisition using Mobile Eyes device is shown in Figure 3.3
Figure 3.3 Iris image acquisition set up using Mobile Eyes
The sample images acquired using above device are shown in Figure 3.4.
36
(a)
(b)
Figure 3.4 Examples of iris images (a) Iris image 7_S1_R_6
(b) Iris image 7_S1_L_7
Iris images were acquired at Biomedical Instrumentation Laboratory, Department of
Instrumentation and Control; College of Engineering Pune following guidelines of
Institutional Review Board (IRB) approved protocol. All subjects participating in the image
acquisition signed a consent form at each acquisition. The majority of the subjects are with
black and brown iris. The age of subjects, ranges between 20 to 30 years. The students from
College of Engineering Pune are the subjects for the created database. Subjects were
informed about the entire process of iris image acquisition to ensure their voluntary
participation. The entire process was explained to the subjects, (how their iris images will be
used in the research, purpose of the study, risks, benefits, confidentiality, etc.). The subjects
were explained the zero ill effects in acquiring their iris images.
COEP.v2 iris database contains total 4800 images of 240 subjects each having 10 images per
eye and both eyes are considered during acquisition. All iris images are captured using
Mobile-Eyes (L1-Identity Solutions) iris camera with Near IR Broadband Illumination, Noncontact capture at 5.8 cm (2.3 in) stored as PNG format with a resolution of 640 × 480.
COEP.v3 iris image database contains same numbers of images as COEP.v2 except the iris
images are captured using I-Scan2 (Crossmatch Technology) iris camera.
We have also created and developed iris image database for clinical diagnosis with emphasis
on Indian subjects for dataset collection.
Iris image analysis for clinical diagnosis has been an active research area in the last few
decades in alternative medicine [8], [9], [10], [40]. However, early research was obstructed
37
by the lack of iris images databases with clinical history of subjects. Nowadays few iris
image databases are created by various research groups and are freely available for further
examination instrument. The most important field of application is the examination of the
anterior segment of the eye including the crystalline lens and the anterior vitreous body.
Supplementary optics such as contact lenses and additional lenses permit observation of the
posterior segments and the iridocorneal angle that are not visible in the direct optical path. A
number of accessories have been developed for slit lamps extending their range of
application from pure observation to measurement, such as for measuring the intraocular
pressure. The documentation of findings on electronic media is increasingly gaining
importance as it provides a convenient medium for keeping track of a disease’s progress. It
also facilitates the communication between physician and patient or between physicians.
We have studied various slit lamps used by Ophthalmologist and the comparison of
specifications / features of slit lamps are provided in Table 3.1.
Table 3.1 Comparison of specifications / features of slit lamps
Manufacturer
Model No.
Magnification
Huvitz
HS 5000
6x, 10x, 16x, 25x, 40x
Carl Zeiss
SL 115
8x, 12x, 20x
Field of View
38.5, 22.2, 15.2, 10.5, 6
mm
0.3~12mm continuous
0~12mm continuous
Cobalt blue, red free, gray,
heat absorption
25 mm~10 mm
Slit Length
Slit Width
Filters
Slit Rotation
0°~180° continuous
Angle of Incidence
Working Distance
Light Source
Light Source Rating
Base
Movements
(Vertical,
Lateral,
Longitudinal)
Power Supply
0°, 5°, 10°, 15°, 20°
80 mm
Halogen lamp
12V, 30W
28mm, 98 mm, 78 mm
100~240V, 50~60 Hz,
2.0A
38
Haag Streit
BX 900
6.5x, 10x, 16x,
25x, 40x
-
0.5, 3.5, 8, 14mm step
0~14 mm continuous
Blue, red free, yellow,
heat absorption
1~8mm continuous
0~8mm continuous
Gray, red free,
blue, heat
absorption
0°~180° continuous
0°~180°
continuous
0°, horizontal
0°, horizontal
73mm
76 mm
Halogen lamp
Halogen lamp
6V, 10W
12V, 30W
30mm,
110mm, 90mm
100~240V, 50~60 Hz
100~240V, 50~60
Hz
Based on specifications / features of slit lamp with digital camera and available resources we
selected Huvitz HIS 5000 device for creation of iris database for clinical diagnosis with
emphasis on diabetic subjects.
Figure 3.5 (a) shows the Huvitz HS 5000 and Figure 3. 5 (b) shows GUI for iris image
acquisition of subjects.
Figure 3.5 (a) Huvitz HS 5000
Figure 3.5 (b) GUI for iris image acquisition
39
Database of iris images for clinical predictions include name of subject, age, sex, clinical
history for diabetic and nondiabetic (includes random and fasting conditions), others clinical
history, etc.
We have collected iris images of subjects (Since April 2008) with clinical history in the
following organizations / Laboratories / Institutes following guidelines of IRB approved
protocol.
i.
Vijay Satav’s Pathological Laboratory Chinchawad, Pune
ii.
National Institute of Ophthalmology Shivajinagar, Pune
iii.
Diabetic Association Pune, Rasta Peth, Pune
All subjects participating in the image acquisition signed a consent form at each acquisition.
The age of subjects ranges from 20 to 90 years. The people from various places visiting
above labs / institutes from Pune are the subjects for the created database. Subjects were
informed about the entire process of iris image acquisition to ensure their voluntary
participation. The entire process was explained to the subjects (how their iris images will be
used in the research, purpose of the study, risks, benefits, confidentiality, etc.). The subjects
were explained the zero ill effects in acquiring their iris images. Table 3.2 shows summary of
iris images in COEP.v1 database.
Table 3.2 Summary of iris images in COEP.v1 database
Clinic/Lab
Normal
Diabetes
Male
Female
Total
Satav’s
Pathology lab
Pune
73
93
93
73
166
NIO
197
104
147
154
301
Diabetic
Association
Pune
186
142
183
145
328
Total
456
339
423
372
795
The age distribution of COEP.v1 is as follows:
Age 20 to 30 years = 1 %
Age 31 to 40 years = 8.26 %
Age 40 to 50 years = 16.74 %
Age 50 to 60 years = 22.80 %
Age 60 to 70 years = 44.70 %
Age 70 to 80 years = 3.90 %
40
Age 80 to 90 years = 2.6 %
Table 3.3 shows comparison between the iris image databases that are available for biometric
purposes
Table 3.3 Comparison between the iris image databases
Database
Example Image
Wavelength
CASIA.v1
Total
Images
756
Varying
distance
No
Acquisition
Device
CASIA
camera
Observations
CASIA.v2
1200
Near
Infrared
No
CASIA
camera
Subset of the
subsequent database
version.
CASIA.v3
22034
Near
Infrared
No
OKI
irispass-h
Images captured with
two different devices.
Contains images with
close characteristics
to the v1 version, with
exception of the
manual pupil filling.
UPOL
384
Visible
No
SONY
DXC-950P
3CCD with
TOPCON
TRC50IA
Completely noise-free
images acquired
with an optometric
framework under high
constrained
environment
BATH
1000
Near
Infrared
No
ISG
LightWise
LW-1.3-S1394
High homogeneous
lighting environment.
Contains essentially
iris obstructions due
to eyelids and
eyelashes.
UBIRIS.v1
1877
Visible
No
Nikon
E5700
Images captured
under heterogenous
lighting
environments. Several
reflections and
obstructions can be
observed.
41
Near
Infrared
Previous filling of the
pupil regions turns
Segmentation much
easier.
UBIRIS.v2
1000
Visible
Yes
Canon
EOS 5D
NICE.I
1000
Visible
Yes
Canon
EOS 5D
NICE.II
2000
Visible
Yes
Canon
EOS 5D
MMU.1
450
Near
Infrared
No
LG EOU
2200
Noise factors avoided.
MMU.2
995
Near
Infrared
No
Panasonic
BMET100US
Noise factors avoided.
WVU
1852
Near
Infrared
No
OKI
irispass-h
Contains poor
lighting, defocus blur,
off angle, and heavy
occluded images.
42
Images captured
under heterogenous
lighting
Environments,
varying distances.
Several reflections
and
obstructions can be
observed.
Images captured
under heterogenous
lighting
Environments,
varying distances,
Wearing glasses. .
Several reflections
and obstructions can
be observed.
Images captured
under heterogenous
lighting environments,
varying distances,
Wearing glasses. .
Several reflections
and obstructions can
be observed.
ND Iris 04
05
64980
Near
Infrared
No
LG EOU
2200, LG
EOU 4000
Contains off-angle,
partial, rotated and
non-iris images and
eyes with contact
lenses.
COEP.v1*
864
Visible
No
Huvitz HS
5000
Contains poor
lighting, defocus blur,
motion blur and heavy
occluded images.
COEP.v2
4800
Near
Infrared
No
L1 Identity
MobileEyes
Noise factors avoided,
Also contains
essentially iris
obstructions due to
eyelids and eyelashes.
COEP.v3
4800
Near
Infrared
No
Crossmatch
I - Scan2
Noise factors avoided,
Also Contains
essentially iris
obstructions due to
eyelids and eyelashes.
*Iris database for clinical diagnosis (Images with clinical history of subject with diabetic
status)
3.3
Summary
This chapter explains the various free iris databases available in public domain to solve the
problem of iris biometrics in real world applications. There are currently seven free available
iris image databases that can be used for biometric purposes: Chinese Academy of Sciences
(CASIA with three distinct versions), Multimedia University (MMU, two versions),
University of Bath (BATH]), University of Olomuc (UPOL), ND Iris 04 05 (Superset of Iris
Challenge Evaluation (ICE), ICE versions of 2005 and 2006), West Virginia University
(WVU) and University of Beira Interior (UBIRIS), whose main characteristics are given in
Table 3.2. At first, it should be stressed that, with exceptions of the UPOL (imaged with an
optometric device) and UBIRIS databases, all the remaining ones contain NIR images.
UBIRIS, NICE I and NICE II data sets contain images acquired at largely varying distances,
illumination, on the move, subject wearing glasses, subject wearing contact lenses and all of
43
them used a flexible image acquisition protocol. UBIRIS, NICE I and NICE II are all noisy
datasets. However, excluding the UBIRIS, NICE I and NICE II database, the remaining
databases contain very moderate levels and types of noisy data. Finally, none of the data sets
contain iris images acquired with clinical history of subject with emphasis on specific
disease(s). We have developed datasets namely COEP.v1 containing iris images of subjects
with clinical history for diabetes and non diabetes. An iris image datasets namely COEP.v2
and COEP.v3 is also developed for biometric application.
*****
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