We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics:
highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. Our work uses standard integrodifferential operators to locate the iris. Then, we process iris image with histogram equalization to compensate for illumination variations.The characterization of iris features is performed by using accumulated histograms. These histograms are built from randomly selected subimages of iris. After that, a comparison is made between accumulated histograms of couples of iris samples, and a decision is taken based on their differences and on a threshold calculated experimentally. We ran experiments with a database of 210 iris, extracted from 70 individuals, and found a rate of succesful identifications in the order of 97 %.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Garza Castañon, Luis E.

Montes de Oca, Saúl

Morales Menéndez, Rubén

English

El Servicio de Difusión de la Creación Intelectual

A Statistical Sampling Strategy for Iris
Recognition
Luis E. Garza Castan˜on1, Saul Montes de Oca2, and
Rube´n Morales-Mene´ndez1
1 Department of Mechatronics and Automation, ITESM Monterrey Campus,
{legarza,rmm}@itesm.mx
2 Automation Graduate Program Student, ITESM Monterrey Campus,
saul montesdeoca@yahoo.com.mx
Av. Eugenio Garza Sada Sur No. 2501
Monterrey, N.L. 64849 Me´xico
Abstract. We present a new approach for iris recognition based on a
random sampling strategy. Iris recognition is a method to identify indi-
viduals, based on the analysis of the eye iris. This technique has received
a great deal of attention lately, mainly due to iris unique characterics:
highly randomized appearance and impossibility to alter its features. A
typical iris recognition system is composed of four phases: image acqui-
sition and preprocessing, iris localization and extraction, iris features
characterization, and comparison and matching. Our work uses stan-
dard integrodifferential operators to locate the iris. Then, we process iris
image with histogram equalization to compensate for illumination vari-
ations.The characterization of iris features is performed by using accu-
mulated histograms. These histograms are built from randomly selected
subimages of iris. After that, a comparison is made between accumulated
histograms of couples of iris samples, and a decision is taken based on
their differences and on a threshold calculated experimentally. We ran
experiments with a database of 210 iris, extracted from 70 individuals,
and found a rate of succesful identifications in the order of 97 %.
1 INTRODUCTION
Iris recognition is an specific area of biometrics. The main intention of biometrics
is to provide reliable automatic recognition of individuals based on the measuring
of a physical characteristic or personal trait. Biometrics can be used for access
control to restricted areas, such as airports or military installations, access to
personal equipments such as laptops and cellular phones, and public applications,
such as banking operations [11]. A wide variety of biometrics systems have been
explored and implemented with different degrees of success. Resulting systems
include different physiological and/or behavioral human features such as: gait,
DNA, ear, face, facial thermogram, hand thermogram, hand vein, fingerprint,
hand shape, palmprint, signature, voice and iris [7,8]. The last one may pro-
vide the best solution by offering a much more discriminating power than others
2biometrics. Specific characteristics of iris such as a data-rich structure, genetic
independence, stability over time and physical protection, makes the use of iris
as biometric well recognized.
In last years, there have been very successful implementations of iris recognition
systems. Differences between them are mainly in the features characterization
step. The golden standard is set by Daugman’s system, with a performance of
99.9 % of matching [3]. Daugman used multiscale quadrature wavelets (Gabor
filters) to extract texture phase structure information of the iris to generate a
2,048-bit iriscode and compared the difference between a pair of iris representa-
tions by their Hamming distance. In [10] iris features are extracted by applying
a dyadic wavelet transform with null intersections. To characterize the texture
of the iris, Boles and Boashash [1] calculated a one dimension wavelet transform
at various resolution levels of a concentric circle on an iris image. In this case the
iris matching step was based on two dissimilarity functions. Wildes [13] repre-
sented the iris texture with a Laplacian pyramid constructed with four different
resolution levels and used the normalized correlation to determine whether the
input image and the model image are from the same class. A Similar method
to Daugman’s is reported in [9], but using edge detection approach to localize
the iris, and techniques to deal with illumination variations, such as histogram
equalization and feature characterization by average absolute deviation. In [5]
iris features are extracted by using independent component analysis. Zhu et al
[14] uses statistical features, mean and standard deviations, from 2D wavelets
transforms and Gabor filters, to make the system more robust to rotation, trans-
lation and illumination variations of images. In [6] a new method is presented
to remove noise in iris images, such as eyelashes, pupil, eyelids and reflections.
The approach is based on the fusion of edge and region information. In [4] an
iris recognition approach based on mutual information is developed. In this work
couples of iris samples are geometrically aligned by maximizing their mutual in-
formation and subsequently recognized. The mutual information was calculated
with the algorithm proposed by Darbellay and Vajda. The decision whether two
compared images belong to the same eye depends on a chosen threshold of mu-
tual information.
In our approach we work directly with the iris information, instead of using a
bank of filters or making a mathematical transformation. These approaches can
be very computationally demanding. We claim than our approach will conduct
to a fast approach for iris recognition, where just a few samples will be needed
to discard many samples in database and allow a more focused sampling in a
reduced set of candidate samples.
In our work, a database with colored high resolution eye images, is processed
to lower the size and transform to a grey levels image. After this, the iris is
located by using integrodifferential operators and extracted by using a trans-
formation from cartesian to polar coordinates. The result of this operation is a
rectangular strip containing just the iris area features. To compensate for illumi-
nation variations, iris strip is processed by histogram equalization. The feature
extraction step is done by randomly sampling square subimages, and building
3an acummulated histogram for each subimage. Every iris is represented by a set
of accumulated histograms. The optimal size of square areas and the number
of features were calculated experimentally. The comparison between iris sample
and database is done by computing the Euclidean distance between histograms,
and according to a threshold calculated also experimentally, we take the decision
to accept o reject the iris sample.
We ran experiments in a database containing 210 samples coming from 70 indi-
viduals and found a rate of succesful matching in the order of 97 %.
2 THE PROPOSED APPROACH
The implementation of our approach relies on the use of eyes images from a
database. These images use a format with color and high resolution, which can
give us some problems with the management of memory. In our database, images
can contain more than 6 Mbytes of information. Then, the first step consists
in down sizing the image (we use 1024x758 bytes), and transform from color
representation to just grey level pixels. This process is sufficient to reveal the
relevant features of iris. Eyes images include samples where iris is free from any
occlusion, as is shown in figure 1, and others with moderate obstruction from
eyelids and eyelashes (Fig. 2).
Fig. 1. Eyes samples without noise
2.1 Iris Localization
The finding of limbic and pupilar limits is achieved with the use of the standard
integrodifferential operator shown in eqn 1.
max
(r, x0, y0)=
∣∣∣∣ ∂∂rG(r) ∗
∮
r,xc,yc
I(x, y)
2pir
ds
∣∣∣∣ (1)
4Fig. 2. Eyes samples with noise (moderate obstruction)
where I(x, y) is an image containing an eye.
The operator behaves as an iterative circular edge detector, and searches over
the image domain (x, y) for the maximum in the partial derivative with respect
to an increasing radius r, of the normalized contour integral of I(x, y) along a
circular arc ds of radius r and center coordinates (x0, y0). The symbol ∗ denotes
convolution and Gσ(r) is a smoothimg function, tipically a Gaussian of scale σ.
This operator behaves well in most cases with moderate noise conditions, but
requires some fine tuning of parameters, in order to deal with pupil reflections,
obscure eyes and excess of illumination. Heavy occlusion (iris area covered more
than 40 %) of iris by eyelashes or eyelids needs to be handled by other methods.
The extracted iris image has to be normalized to compensate for pupil dilation
and contraction under illumination variations. This process is achieved by a
transformation from cartesian to polar coordinates, using equations 2 and 3.
The output of this transformation is a rectangular image strip , shown in Fig.
5(a).
x(r, θ) = (1− r)xp(θ) + rxs(θ) (2)
y(r, θ) = (1− r)yp(θ) + rys(θ) (3)
where x(r, θ) and y(r, θ) are defined as a linear combination of pupil limits
(xp(θ), yp(θ)) and limbic limits (xs(θ), ys(θ)). r is defined in the interval [0, 1],
and θ in the interval [0, 2pi].
2.2 Features Extraction
The iris image strip obtained in previous step, is processed by using an histogram
equalization method, to compensate for differences in illumination conditions.
The main objective in this method is that all grey levels (ranging from 0
to 255) have the same number of pixels. Histogram equalization is obtained by
working with the accumulated histogram, shown in eqn 4.
5H(i) =
i∑
k=0
h(k) (4)
where h(k) is the histogram of the kth grey level.
A flat histogram, where every grey level has the same number of pixels, can be
obtained by eqn 5.
G(i′) = (i′ + 1)
N ∗M
256
(5)
Where N and M are the image dimensions and 256 is the number of grey
levels. An example of application of histogram equalization method over the iris
strip in figure 5(a), is shown in figure 5(b).
(a)
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
(b)
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
Fig. 3. (a) Extracted iris strip image.(b) Iris strip processed by the histogram equalization
method
2.3 Comparison and Matching
In our method, the iris features are represented by a set of accumulated his-
tograms computed from randomly selected square subimages of iris strip (see
Fig. 6). An accumulated histogram represents a feature and is built by using
equation 4. The complete iris is represented by a set of accumulated histograms,
one of them for every subimage. The optimal size of the number of features
and subimage size, were determined empirically by experiments. A decision to
accept or reject the iris sample is done according to the minimum Euclidean
distance calculated from the comparison of iris sample and irises database, and
also according to a threshold. Figure 8 shows the structure of this phase.
6Fig. 4. iris strip image showing a random selection of areas
Fig. 5. The process of comparison and matching
73 Experiments
Experiments were ran for a database with 210 samples coming from 70 indi-
viduals. In a first step, a set of experiments was scheduled to produce the best
acceptance/rejection thresholds (Euclidean distance). In a second step, a set of
10 experiments were conducted by fixing both: the size of subimage area and the
number of features or histograms. In every experiment a random iris sample was
taken, and a random database of 100 samples was formed without duplicates.
The percent of successful identifications reported is the mean of those 10 exper-
iments. The Fig. 8 shows the accuracy on the accept/reject decision taken. The
best results of 97.18 % were obtained with an square subimage area of 25 pixels
and 210 features. False positive errors were 0.26 % and false negatives errors
were 2.56 %.
0
20
40
60
80
0
50
100
150
200
250
300
80
82
84
86
88
90
92
94
96
98
area sizenumber of iris features (areas)
A
cc
u
ra
cy
 (%
)
Fig. 6. Results from experiments with different number of features and size of areas
The Fig. 9 (a) shows the distribution observed in function of two types of
persons, ”authentics” and ”impostors”. This distribution estimation treats only
one-to-one verification mode. We can observe a significant overlapping area,
which in turns avoid more accurate scores of recognition. In Fig. 9 (b) is shown
the ROC curve that is a plot of genuine acceptance rate against false acceptance
rate. Points in the curve denote all possible system operating states in different
tradeoffs. Fig. 9 (b) shows an acceptable performance of our method.
80.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
0.00% 0.05% 0.10% 0.15% 0.20% 0.25% 0.30% 0.35% 0.40% 0.45% 0.50%
False Match Rate (%)
F
a
ls
e
 N
o
n
-
M
a
t
c
h
 R
a
t
e
 (
%
)
  
  
  
  
  
(a) (b)
Fig. 7. (a) Probability Distribution of accumulated histograms distance for authentic per-
sons (left) and impostors (right). (b) Receiving Operating Characteristic curve
4 Conclusions and Future Works
A new approach for iris recognition has been presented. The novel contribution
relies on the feature characterization of iris by the use of a sampling technique
and accumulated histograms. We work directly with the iris information, instead
of using a bank of filters or making a mathematical transformation. Both ap-
proaches can be very computationally demanding. We claim than our approach
will conduct to a fast approach for iris recognition, where just a few samples
will be needed to discard many samples in database and allow a more focused
sampling in a reduced set of candidates. In our proposal iris image is sampled in
a specific number of randomly selected square areas or subimages. From every
subimage an accumulated histogram is built, and the whole iris is represented
by a set of accumulated histograms. The matching step consists then in a com-
parison between histograms, and the decision to accept or reject a user is taken
based on the minimum difference between iris samples , and a threshold calcu-
lated experimentally.
We have found a rate of successful identifications in the order of 97 %.
In future works we will address two aspects: first, the determination of minimal
number of samples and the size of samples, to identify accuratelly a person, and
second, we are looking a more uniform sampling strategy to avoid redundant
information by overlapping of samples.
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A statistical sampling strategy for iris recognition

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