Robust Iris Segmentation under Unconstrained Settings
ao C. Monteiro, H
elder P. Oliveira, Ana F. Sequeira and Jaime S. Cardoso
INESC TEC (formerly INESC Porto) and Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
Biometrics, Iris Segmentation, Unconstrained Environment, Gradient Flow, Shortest Closed Path.
The rising challenges in the field of iris recognition, concerning the development of accurate recognition
algorithms using images acquired under an unconstrained set of conditions, is leading to the a renewed interest
in the area. Although several works already report excellent recognition rates, these values are obtained by
acquiring images in very controlled environments. The use of such systems in daily security activities, such
as airport security and bank account management, is therefore hindered by the inherent unconstrained nature
under which images are to be acquired. The proposed work focused on mutual context information from iris
centre and iris limbic contour to perform robust and accurate iris segmentation in noisy images. A random
subset of the UBIRIS.v2 database was tested with a promising E
classification rate of 0.0109.
In almost everyone’s daily activities, personal iden-
tification plays an important role. The most tradi-
tional techniques to achieve this goal are knowledge-
based and token-based automatic personal identifica-
tions. Token-based approaches take advantage of a
personal item, such as a passport, driver’s license,
ID card, credit card or a simple set of keys to dis-
tinguish between individuals. Knowledge-based ap-
proaches, on the other hand, are based on something
the user knows that, theoretically, nobody else has ac-
cess to, for example passwords or personal identifica-
tion numbers.Both of these approaches present obvi-
ous disadvantages: tokens may be lost, stolen, forgot-
ten or misplaced, while passwords can easily be for-
gotten by a valid user or guessed by an unauthorized
one (Jain et al., 2000). In fact, all of these approaches
stumble upon an obvious problem: any piece of ma-
terial or knowledge can be fraudulently acquired.
Biometrics represents a return to a more natural
way of identification. Testing someone by what this
someone is, instead of relying on something he owns
or knows seems likely to be the way forward.
Several biological traits in humans show a con-
siderable inter-individual variability: fingerprints and
palmprints, the shape of the ears, the pattern of the
iris, among others. Biometrics works by recogniz-
ing patterns within these biological traits, unique to
each individual, to increase the reliability of recogni-
tion. The growing need for reliability and robustness,
raised some expectations and became the focal point
of attention for research works on biometrics. The
choice of a specific biometric trait is weighted by a
set of qualitative values that describe its overall qual-
ity: universality, uniqueness, collectability and per-
manence (Jain et al., 2000). With all these variables
in mind, the iris presents itself as a leading candidate
to become the standard biometric trait: it is universal,
the variability is huge which assures the uniqueness
for each individual, apart from being an organ easily
accessible and very difficult to modify.
Even though excellent rates of recognition are
found in literature (Daugman, 2006) , these results
are associated with a set of acquisition conditions that
constrain the quality of the tested images. The ma-
jority of the developed iris recognition systems rely
on near-infrared (NIR) imaging rather than visible
light (VL). This is due to the fact that fewer reflec-
tions from the cornea in NIR imaging result in maxi-
mized signal-to-noise ratio (SNR) in the sensor, thus
improving the contrast of iris images and the robust-
ness of the system. NIR imaging, however, presents
a series of hazards, as no instinctive response (such
as blinking) is triggered in response to excessively
strong illumination.Another typically imposed con-
straint to the user of an iris recognition system is the
need to stop-and-stare at a close distance to the sensor
(i.e. user collaboration). These factors create impor-
tant limitations to the applicability of iris recognition
algorithms in real-life conditions, such as military ap-
plicationsor bank account management.The develop-
Monteiro J., Oliveira H., Sequeira A. and Cardoso J..
Robust Iris Segmentation under Unconstrained Settings.
DOI: 10.5220/0004281701800190
In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP-2013), pages 180-190
ISBN: 978-989-8565-47-1
2013 SCITEPRESS (Science and Technology Publications, Lda.)
ment of iris recognition algorithms that are capable of
encompassing such limitations has been gaining focus
in recent years.
In this work we focus on iris segmentation, as pro-
posed in (Daugman, 1993). Iris segmentation con-
sists on the detection of the two defining contours of
the iris region. In the eye region, three main regions
can be easily distinguished: the sclera, also known as
the white of the eye, is the most easily distinguishable
part of the eye, surrounding the iris, the coloured re-
gion of the eye; inside the iris, a darker region is dis-
tinguishable, corresponding to the pupil, the region
through which light enters the eye. Besides its dis-
tinctive properties as a biometric trait, the iris is also
a contractile structure responsible to adapt the size of
the pupil, so as to regulate the amount of light that
enters the eye. Two main contours can be defined as
the separating boundaries of the three aforementioned
regions: the limbic contour separates the iris from the
sclera, and the pupillary contour, the iris from the
pupil. The detection of these contours is the main goal
of segmentation and an essential step in the develop-
ment of high accuracy recognition systems.
We argue that iris segmentation can benefit from
the simultaneous detection of the iris centre and iris
external contour. When performed independently,
both tasks are nontrivial since many other parts of
the image may be falsely detected. However, the two
tasks can benefit greatly from serving as context for
each other. Central to our method to detect iris cen-
tre candidates is the use of gradient flow information
with a specific gradient vector field template; the de-
tection of the limbic contour relies on the search of
strong closed contours around the centre candidates.
The remainder of this paper is organized as fol-
lows: Section 2 summarizes relevant works concern-
ing iris segmentation; Section 3 includes an algorithm
overview; the theoretical basis behind the developed
algorithm; a detailed analysis of the different steps of
the limbic contour segmentation algorithm; Section 4
presents the obtained results and finally the conclu-
sions and future prospects are summarized in Sec-
tion 5.
The original approach to the segmentation task was
proposed by Daugman (Daugman, 1993) and con-
sisted in the maximisation of an integro-differential
operator. In a different approach, Wildes (Wildes,
1997) suggested a method involving edge detection
followed by circular Hough transform (CHT). For
years, several works in the iris biometrics field fo-
cused on Daugman’s and Wilde’s algorithms, present-
ing variations at many levels.
One example is the CHT-based method used for
the segmentation step in Masek’s algorithm (Masek,
2003). Ma et al. (Ma et al., 2004) created a sys-
tem that mixed both the CHT segmentation approach
and the rubber sheet model normalization, introduc-
ing some concepts like pre-processing of iris images
for specular reflection removal.
The integro-differential operator and the CHT are
still widely used for segmenting iris images, offering
good segmentation accuracy but also computational
complexity. Radman et al. (Radman et al., 2012) ad-
dresses a simple solution for this problem by local-
izing the initial center of the pupil using a circular
Gabor filter (CGF).
In the work of He et al. (He et al., 2009), an
Adaboost-cascade iris detector is built to extract a
rough position of the iris centre and then the centre
and radius of the circular iris are localised by employ-
ing an elastic model named “pulling and pushing”.
The segmentation of the pupil and iris by fitting a ro-
tated ellipse, after a sequence of procedures for com-
pensating the detected noise, was proposed by Zuo
and Schmid (Zuo and Schmid, 2010).
Since iris boundaries are often not circular or el-
liptical, curve fitting techniques can be valuable to
approximate real iris contours (Proenc¸a et al., 2010).
To further improve segmentation performance, recent
methods attempted to use active contour models to ac-
curately localise irregular iris boundaries (Daugman,
2007; Vatsa et al., 2008; Shah and Ross, 2009). The
approach taken by Chen et al. (Chen et al., 2010) con-
sisted in detecting the sclera region of the eye, thresh-
olding and filtering the image to detect a rectangular
region for iris localization. An edge map of the region
of interest is then obtained with a horizontal Sobel op-
erator, and a dynamic programming variation of the
CHT algorithm was implemented to detect the limbic
boundary. This method corrects the non-circularities
of the off-angle iris and combines the intersection of
circles obtained by the two CHT algorithms and a
linear Hough transform to perform eyelid detection.
More recently, Pawar et al. (Pawar et al., 2012) ap-
plied geodesic active contours to perform segmenta-
Some works use texture analysis to perform seg-
mentation. Sanchez-Avila et al. (Sanchez-Avila et al.,
2002) published a work based on dyadic wavelet
transform zero-crossing as iris signature where im-
ages were pre-processed by histogram stretching (im-
proving contrast between pupil, iris and sclera) to aid
the limbic boundary detection and then, the same al-
gorithm is used inside its area to detect the pupillary
boundary. Nabti and Bouridane’s work (Nabti and
Bouridane, 2008) is based in a multiscale approach,
using Gabor filters and wavelet transform coefficients,
to improve edge detection process that determines the
success of iris segmentation.
Gradient vector field based methods have ap-
peared in literature such as in the work of Chen et
al. (Chen et al., 2011). In this work gradient flow
around the iris center plays an important role in the
segmentation of the limbic contour.
When analysing most of the methods cited in the
literature, it is possible to detect some main draw-
backs. In almost all of these methods, inner and outer
boundaries, eyelashes and eyelids are detected in dif-
ferent steps, causing a considerable increase in pro-
cessing time of the system. Usually, the inner and
outer boundaries are detected by circle fitting tech-
niques. This is a source of error, since the iris bound-
aries are not exactly circles and in noisy situations, the
outer boundary of iris may not present sharp edges.
In some of the aforementioned algorithms, there are
a lot of implicit or explicit assumptions about the ac-
quisition process, which are no longer valid in uncon-
strained acquisition scenarios. Therefore, some of the
promising results reported in the literature must be
taken with caution and reassessed under these new,
more challenging, conditions.
In recent years it has been recognized that the
path forward, regarding iris recognition, is the de-
velopment of algorithms that can work independently
of subject collaboration and proper NIR illumina-
tion conditions, in order to achieve robust (i.e. ac-
curate even with noisy images) and unconstrained
(i.e. accurate for several sets of acquisition condi-
tions: distance, movement, illumination) iris recog-
nition and, in this way, become a real-world applica-
ble method (Ross, 2010). This paradigm shift led to
the rise of new trends in the research of iris recogni-
tion, for example, exploring VL illumination instead
of NIR.
Researchers are now paying more attention to the con-
text to aid visual recognition processes. Context plays
an important role in recognition by the human visual
system, with many important visual recognition tasks
critically relying on it.
The proposed work aimed to accomplish accurate
iris segmentation by using simultaneously acquired
information from two main sources: iris centre and
limbic contour. Both sources contribute to discrimi-
nate between a series of iris segmentation candidates.
Context information regarding typical iris character-
istics in eye images, namely colour and shape, repre-
sented the basis of the developed algorithm. By using
more than a single source of information, we aimed to
lower the misdetection of areas likely to be wrongly
segmented, such as eyebrows and glass frames.
3.1 Algorithm Overview
The main steps of the proposed algorithm are system-
atised in Figure 1. A simplification is adopted in rela-
tion to the main rationale outlined above. The simul-
taneous detection of the iris centre and limbic contour
will be addressed by first over-detecting centre candi-
dates, followed by a contour detection around each of
The centre candidates are estimated by a method
resembling the use of convergence index filters (Ko-
batake and Hashimoto, 1999). Next, a window cen-
tred in each candidate is converted into the polar do-
main and a shortest path algorithm is used to deter-
mine good closed paths around the centre. Using
combined data from the centre and respective contour,
the best pair centre/contour is selected.
Figure 1: Flowchart of the proposed iris segmentation algo-
Typical iris images present two very distinct re-
gions: a high intensity region corresponding to the
eye and the skin, and the iris region, at least partially
circular and lower in intensity. These two sources of
knowledge can be presented separately but are intrin-
sically connected. The fact that the iris is a darker
region against a brighter background translates into a
specific divergent gradient orientation from its centre.
At the same time the limbic contour (iris outer edge)
will present a high gradient magnitude as well as a
closed shape. The approach taken in this work was
that of detecting pairs of iris centre and limbic contour
candidates that maximise a quality factor weighted by
the aforementioned combined knowledge.
3.2 Iris Centre Detection
Iris centre candidates are detected using a template
matching algorithm based on gradient vector field ori-
entation. Theoretically the gradient is a vector field
that points in the direction of the greatest rate of in-
crease of a scalar field. Considering an image as a
scalar field, it is easy to perceive the gradient as a vec-
tor field that points from darker regions (of lower in-
tensity) towards brighter regions (of higher intensity).
Figure 2(b) depicts a simple example of gradient vec-
tor field orientation on a synthetic image. The ex-
pected behaviour of vectorial divergence from darker
regions to brighter regions is observed. These obser-
vations can be easily extrapolated to typical eye im-
(a) (b)
Figure 2: Gradient orientation vector field in synthetic im-
ages. Notice how the vector field diverges from darker re-
gions and converges to brighter regions.
The iris is surrounded by two distinct higher inten-
sity regions: the sclera and the skin. With this in mind
a divergent gradient orientation is expected from the
center of the iris towards the aforementioned brighter
regions, as observed in Figure 3(b).
(a) (b)
Figure 3: The iris centre detection is based on two vector
fields: a) Template vector field and b) Gradient orientation
vector field.
The centre candidates are, thus, detected by com-
puting the cross-correlation, c
, between the gradi-
ent vector field orientation and the divergent template
vector field depicted in Figure 3(a). The c
are calculated as:
=( f g)[n]
de f
[m]g[n + m] (1)
where f and g represent the gradient orientation vec-
tor field and the template vector field respectively.
The resulting c
matrix can be graphically repre-
sented as exemplified in Figure 4(a), where the values
range from 1 to 1, with 1 being represented in blue
and 1 in red. The centre candidates are detected as the
N local maxima with the highest c
Figure 4: Cross-correlation results on the synthetic image
from Figure 2(a).
3.3 Limbic Contour Detection
In the proposed method for limbic boundary detection
we consider the image grid as a graph, with pixels
as nodes and edges connecting neighbouring pixels.
With this in mind the proposed algorithm defines a
limbic contour candidate as the best closed contour
around a given centre candidate.
The computation of this best contour is simplified
by working in polar coordinates (relative to each iris
centre candidate). In this domain a closed contour
around a given point becomes a curve from the left
side of the polar image (θ = 0
) to the right side of
the same image (θ = 360
). With the aforementioned
consideration of the image as a graph, computation
of the best closed contour becomes computation of
the shortest left-to-right path in polar domain. To bet-
ter understand the proposed limbic contour detection
algorithm, we start by introducing some graph con-
cepts (Oliveira et al., 2012).
3.3.1 Graph Concepts
A graph G = (V, A) is composed of two sets V and
A. V is the set of nodes, and A the set of arcs (p, q),
p,q V . The graph is weighted if a weight w(p,q)
is associated to each arc. The weight of each arc,
w(p, q), is a function of pixels values and pixels rela-
tive positions. A path from vertex (pixel) v
to vertex
(pixel) v
is a list of unique vertices v
,. .. , v
, with
and v
corresponding to neighbour pixels. The to-
tal cost of a path is the sum of each arc weight in the
A path from a source vertex v to a target vertex u
is said to be the shortest path if its total cost is min-
imum among all v-to-u paths. The distance between
a source vertex v and a target vertex u on a graph,
d(v,u), is the total cost of the shortest path between v
and u.
A path from a source vertex v to a sub-graph is
said to be the shortest path between v and if its total
cost is minimum among all v-to-u paths. The
distance from a node v to a sub-graph , d(v,), is
the total cost of the shortest path between v and :
d(v,) = min
d(v,u). (2)
A path from a sub-graph
to a sub-graph
is said to be the shortest path between
its total cost is minimum among all v
paths. The distance from a sub-graph
to a sub-
, d(
), is the total cost of the shortest
path between
) = min
d(v,u). (3)
3.3.2 Algorithm for Limbic Contour Detection
Intuitively, the limbic boundary appears as a closed
contour in the image, enclosing the iris centre, and
over pixels with a strong transition in the grey-level
values. Assuming that paths through pixels with high
gradient are preferred over paths through low gradient
pixels, the limbic contour can then be found among
the shortest closed paths enclosing the iris centre can-
A difficulty with searching for the shortest closed
path enclosing a given point C is that small paths, col-
lapsing in the point C, are naturally favoured. We
overcome that difficulty by working on polar coordi-
nates. We assume that the origin of the coordinates is
the candidate iris centre.
A circular window centred in each candidate is
transformed to polar coordinates. A closed path in
the original Cartesian coordinates (Figure 5(a)) is
transformed into a path from left to right margins in
the window in polar coordinates, starting and end-
ing in the same row of the transformed window (Fig-
ure 5(b)).
Note that the main assumptions are a) the candi-
date centre lies within the true limbic contour; b) the
limbic contour constitutes a closed path over pixels of
strong gradient. The limbic contour is not necessar-
ily circular and the candidate centre does not need to
match the true iris centre for a correct contour detec-
tion. As long as one centre candidate lies within the
iris region one closed contour around it will be de-
tected, regardless of the distance between the detected
iris centre candidate and the real iris centre.
3.3.3 Computation of the Shortest Closed Path
In spite of the efficiency of the computation of the
shortest path between the whole left and right mar-
gins, or between two pre-defined points in the mar-
gins, or between one of the margins and a pre-defined
Figure 5: a) Original limbic contour in Cartesian coordi-
nates; b) corresponding left-to-right path in the polar do-
point in the other margin, the search for the short-
est path between the left and right margins with the
constraint that the path should start and end in the
same row seems to increase the complexity of the pro-
cedure. As typical, optimization with constraints is
more difficult than without.
Had one been interested in the simple shortest path
between the left and right margin and the computation
would be very efficiently performed using dynamic
programming. Assuming the simplifying assumption
that the vertical paths do not zigzag back and forth, up
and down, in the transformed image, the search may
be restricted among connected paths containing one,
and only one, pixel in each column between the two
Formally, let I be an N
window (after polar
coordinate transform) with N
columns and N
define an admissible path to be
s = {(x, y(x))}
, s.t. x |y(x) y(x 1)| 1,
where y is a mapping y : [1,··· , N
] [1, ··· , N
That is, an admissible path is an 8-connected path of
pixels in the image from left to right, containing one,
and only one, pixel in each column of the image.
The first step is to traverse the image from the sec-
ond column to the last column and compute the cumu-
lative minimum cost C for each entry (i, j):
C(i, j) = min
C(i 1, j 1) + w(p
i1, j1
; p
i, j
C(i 1, j) + w(p
i1, j
; p
i, j
C(i 1, j + 1) + w(p
i1, j+1
; p
i, j
where w(p
i, j
; p
) represents the weight of the edge
incident with pixels at positions (i, j) and (l, m). At
the end of this process,
, j)
indicates the end of the minimal connected path.
Hence, in the second step, one backtrack from this
minimum entry on C to find the optimal path.
Note that this procedure gives not only the shortest
path between the left and right margins but also yields
the shortest path between any point in the right mar-
gin and the whole left margin: for any point (N
, j)
in the right margin, C(N
, j) indicates the cost of the
shortest path between (N
, j) and the whole left mar-
gin, see Figure 6. Finally, it should be clear how to
change the initial conditions of the above procedure to
yield the shortest path between two pre-defined points
in the opposite margins.
(a) (b)
Figure 6: Example of shortest path starting point detection.
(a) shows all paths from the left margin to the right margin
and (b) all the paths from the right margin to the left mar-
gin. As is easily deductable, at least one closed contour will
result from this process.
Unfortunately, the computation of the shortest
path constrained to start and end in the same row (cor-
responding to closed contours in the original window)
does not seem amenable to such an efficient proce-
dure. The brute force solution of computing the short-
est path between the i-point in the left margin and the
i-point in the right margin, for i = 1···N
, and taking
the minimum, is not compatible with requirements of
near real-time in our application.
Noting that if j and ` are two distinct points in
the right margin, then the shortest paths between each
of these points and the whole left margin do not in-
tersect, it is trivial to conclude that there is at least
one point m in the right margin for which the shortest
path between m and the whole left margin starts also
at row m. Note that the paths correspond to closed
paths in the original window in Cartesian coordinates
(not necessarily including the shortest one). Similarly,
interchanging the role of the left and right margin, it
is possible to obtain at least one point n in the left
margin for which the shortest path to the whole right
margin is closed. By computing all the paths from
the left to the right margin (and vice-versa), a set of k
closed contours is obtained for each centre candidate.
The procedure is illustrated in Figure 6.
3.3.4 Design of the Weight Function
The weight of an edge in the graph is a function of the
values of the incident nodes (pixels). We start by com-
puting the derivative in the radial direction (centred in
the iris candidate position) in the original space, us-
ing a 3-point numerical differentiation, as defined in
Eq. (4).
(r) =
I(r + h) I(r h)
In the graph, to each edge incident with 4-
neighbouring pixels correspond a weight determined
by the derivative value of the two incident pixels, ex-
pressed as an exponential law, presented in Eq. (5).
f (g) = f
+ ( f
exp(β (255 g)) 1
exp(β 255) 1
with f
= 2, f
= 32, β = 0.0208 and g is the min-
imum of the derivative computed on the two incident
pixels. For 8-neighbour pixels the weight was set to
2 times that value. The parameter β was experimen-
tally tuned using a grid search method. The remain-
ing parameters were manually optimised in some of
our previous works (Oliveira et al., 2012).
3.4 Best Pair Centre/Contour
From the previously described steps a set of cen-
tre/contour candidate pairs (Cp) is built. An example
of such candidate pairs is depicted in Figure 7, where
the yellow circles represent the centres and the purple
curves the limbic contour candidates.
Figure 7: Example of the centre/contour set of candidates.
The centre candidates are represented by yellow circles, the
detected contours by purple curves and the ground-truth iris
centre by a white cross.
The joint decision for the centre and contour is
taken to maximise the joint probability of the individ-
ual parts. In here, we assume that the joint probability
is a monotonous function of the product of individual
measures of quality, combined in an overall quality
factor, Q. The discrimination between candidates is
performed by choosing the pair with the highest Q.
The quality factor is given by:
Q(C p) =
µ(C) ·ρ
|1 S(C)|
where µ(C) is the mean derivative alongside the con-
tour, ρ
is the cross-correlation value of the centre
candidate, and S is the shape factor of the contour
(with perimeter P and area A), given by:
S(C) =
4π ·A
This way the best centre/contour pair, Cp
, is se-
lected based on mutual information from both iris
centre and limbic contour quality.
3.5 Upper Eyelid Approximation
Eyelids represent one of the most common noise fac-
tors on images acquired under unconstrained settings
when compared with images acquired in controlled
environments. Even though the proposed algorithm
presents no shape constraints, dark regions, such as
eyelashes and shadows, generally pose difficulties to
the shortest path algorithm. To encompass such dif-
ficulties a simple eyelid approximation algorithm is
proposed, based, once again on graph notions. The
algorithm is very similar to the one proposed for lim-
bic contour segmentation. It presents two main differ-
The shortest left-to-right path is computed on the
original image in Cartesian coordinates;
No cost function is designed, and the cost associ-
ated with each edge is now given by the minimum
intensity of each incident pixel.
With such premises the left-to-right shortest path
algorithm, applied to the original iris image, will pref-
erentially stick to low intensity left-to-right curves.
As the eyelashes often accumulate on the upper eye-
lid, creating a low intensity region over the iris, the
shortest path will tend to traverse such region.
The lower eyelid was not detected as the eyelashes
tend to be longer and considerably more dense in the
upper eyelid, than in the lower eyelid.The observed
contrast between iris and the lower eyelid is, thus,
enough so as not to mislead the proposed algorithm.
An example of both successful and unsuccessful eye-
lid localisation is depicted in Figures 8(a) and 8(b)
4.1 Tested Dataset
The proposed algorithm was tested on the UBIRIS.v2
iris image database (Proenc¸a et al., 2010). Images
in UBIRIS.v2 were captured under non-constrained
conditions (at-a-distance, on-the-move and on the vis-
ible wavelength), with corresponding realistic noise
(a) (b)
Figure 8: Example of: a) successful eyelid localisation and
b) unsuccessful eyelid localisation.
(a) (b) (c) (d)
(e) (f) (g) (h)
Figure 9: Examples image classes in the UBIRIS.v2
database: a) Heavily occluded; b) Heavily pigmented; c)
Glasses occlusion; d) Reflection occlusion; e) Off-angle; f)
Rotated eye; g) Black subjects and h) Normal.
factors. Figure 9 depicts some examples of these
noise factors (reflections, occlusions, pigmentation,
etc.). A subset of the original database, composed by
802 images from 36 distinct individuals was created.
All images and individuals were randomly selected,
so as to better encompass the widest possible range
of noise factors. All images from the created subset
were manually annotated for the limbic and pupillary
contour, as well as for the geometric center of the iris
4.2 Iris Centre Candidate Detection
The accuracy of the centre candidate detection step
was analysed by computing the distance between the
manually annotated iris centre and each of the N cen-
tre candidates. In the proposed work we use N = 4
as this value guaranteed that at least one candidate
lied inside the iris/pupil region, for every image in the
tested dataset. The Euclidean distance between each
center candidate and the manually annotated ground-
truth centre was computed. The iris centre detection
accuracy for a particular image corresponded to the
minimum of these distances.
A mean distance of 6.29 ± 5.71 pixels was ob-
tained for the tested dataset. Considering that the
mean iris radius of the tested dataset was 58.71 ±
17.45 pixels this result might seem not that promis-
ing. The observed deviations of the center candidates
from the real iris center arise mainly from two causes:
a) the partial occlusion of the iris by the eyelids results
in a deviation from an ideal circular shape and b) the
extent to which specular reflections contaminate the
iris region causes the gradient flow to diverge towards
those regions instead of the sclera.
However, given how the limbic contour detection
algorithm is designed, there is no need to achieve per-
fect accuracy on the real iris centre with any of the
detected candidates. As long as ones of the candi-
dates lies inside the iris/pupil region, the detection of
a closed contour around it (not necesseraly centred on
it) is guaranteed.
4.3 Best Centre/Contour Pair
The discriminative performance of the proposed qual-
ity factor, Q(C p), was analysed by computing the
misdetection ratio, M
. This value corresponds to
the ratio between the number of images where the
best centre/contour pair was not correctly discrim-
nated and the total number of tested images. To prove
that mutual context information improves results ob-
tained by singular sources of information, the M
ues for each Q(Cp) parameter were tested individu-
The centre/contour pair with maximum ρ
The centre/contour pair with maximum C value
The center/contour pair with a S(C) value closest
to 1
The M
values for each individual parameter and
for the quality factor are summarized in Table 1. As
it can be seen the quality factor overperforms every
singular parameter by a considerable margin, pre-
senting a 2.12% value of M
. Both gradient and
cross-correlation based discriminations presented in-
termidiate results, demonstrating limited discrimina-
tory capacity when compared to the mutual context
results of the quality factor. Circularity (|1 S(C)|)
presents, by far, the worst individual discriminative
performance. This observation may lead to one of
several conclusions. Either circularity is not a good
parameter to be used in the scope of mutual con-
text information, or its effect is only observable when
combined to other sources of knowledge. As no at-
tempt was made of testing combinations of two of the
three suggested parameters, the true relevance of cir-
cularity, as far as discrimination is concerned, cannot
be fully asserted.
Table 1: Misdetection ratios observed when the discrimina-
tion is performed with each individual parameter and with
the proposed quality factor.
Parameter M
C 0.0860
|1 S(c)| 0.629
Q 0.0212
4.4 Limbic Contour Segmentation
To evaluate the segmentation accuracy of the previo-
suly discriminated best limbic contour candidates a
series of metrics were computed. All these metrics,
listed below, were computed for the initial contour
and for the contour after eyelid detection, so as to as-
sert the advantages of this last process. Table 2 sum-
marizes the most relevant results:
Mean, median and maximum (Hausdorff) dis-
tance, in pixels, between the detected limbic con-
tour and the manually annotated ground-truth
and E
errors, as presented in the NICE.I con-
test (
Mean percentage of false iris (FIR) and false non-
iris (FNIR) segmented pixels
The first three measurements refer to point-to-
point distances between the two referred contours.The
histogram of errors and the corresponding box plots
are depicted in Figures 10(a) and 10(b), respectively.
The information presented in the histogram shows
that, besides the percentage of images where the qual-
ity factor failed the discrimination of the best cen-
tre/contour pair (and thus the largest distances were
observed), the segmentation errors are relatively low.
The effect of eyelid detection is evident in both re-
sults. The histogram of errors after eyelid detection
reveals an increased concentration of errors towards
lower values. This observation is also supported by
the observation of the boxplot results. Lower mean
and standard deviation values further corroborate the
significant improvement introduced by eyelid detec-
tion. The observed influence of the upper eyelid on
the segmentation results shows that its detection is a
key step of the proposed algorithm. As the eyelashes
often present an higher contrast with the skin than
the iris with the eyelashes, it is only safe to assume
that a gradient weighted shortest path algorithm will
tend to prefer the eyelash-skin boundary to the iris-
eyelash boundary. Eyelid detection compensates for
this fact and results in a significant improvement in
all the tested metrics.
(a) (b)
Figure 10: Distribution of the segmentation errors in the tested dataset: a) Histogram of errors (in pixels) and b) boxplots of
the error distributions. All the results are presented before and after eyelid detection.
Table 2: Summary of the most relevant segmentation quality measurements before and after eyelid localisation.
Mean Median Hausdorff E
Before eyelid detection 7.11 ±5.11 4.96 19.47 0.0200 0.0923 0.1814 0.00325
After eyelid detection 4.86 ±2.96 4.18 12.50 0.109 0.0374 0.0690 0.00583
Pixels [0 1]
In 2008 Hugo Proenca and Luis Alexandre, from
Universidade of Beira Interior (UBI), Portugal, pro-
moted the NICE.I Contest (
This contest aimed to “evaluate the robustness to
noise of iris segmentation and noise detection algo-
rithms, toward iris recognition systems within less
constrained image capturing conditions, eventually to
covert ones, in the near future”. The NICE results
represent the great majority of the already available
segmentation results using the UBIRISv2 database.
However the evaluation parameters of the aforemen-
tioned contest are based on two principles that signif-
icantly vary from our proposed approach:
1. The segmentation of the iris region of the eye was
based both on the detection of the limbic and the
pupillary contours. In our work we performed no
segmentation of the pupillary contour, as we argue
that performing recognition regardless of this step
might prove as the path forward, as far as uncon-
strained iris recognition is concerned. The ratio-
nale behind such decision is based on the fact that
the contrast between the pupil and the iris is ex-
tremely dependent on a set of hardly controlable
factors (illumination, iris pigmentation, obstruc-
tions, etc.), thus creating a serious challenge as
far as the development of robust segmentation al-
gorithms is concerned.
2. The final segmentation results are evaluated as
number of pixels correctly classified as iris. This
description takes in consideration the detection of
noisy areas (reflections or eyelashes for example)
which surpasses the scope of the proposed work.
With these two points in mind it is obvious that a
direct comparison with the NICE.I segmentation re-
sults is not possible. However the two metrics sug-
gested for the evaluation of iris segmentation in the
contest were adapted for the evaluation of the pro-
posed algorithm.
The mean E
and E
errors for the tested dataset
of images are presented in Table 2. The effect of
eyelid detection was already ascertained through the
analysis of the point-to-point results, but it is of rele-
vance to note that the NICE.I metrics corroborate the
previous conclusions.
The obtained E
error is lower than all the re-
ported errors in the NICE.I contest (summarized in
Table 3). However such a direct comparison will only
be possible when noise detection and puppilary es-
timation are incorporated in the present algorithm.
Nevertheless, these preliminary results seem to indi-
cate some promise regarding the chosen approach.
The obtained E
value leads to some interesting
conclusions. The value presented in Table 3 is the
result of a mean FIR of 0.069 and a mean FNIR of
0.0058. A higher FIR value was to be expected as, in
most cases, the number of iris pixels in the UBIRIS.v2
images is considerably smaller than the number of
non-iris pixels. A 0.0058 FNIR is an excellent in-
(a) (b)
(c) (d)
Figure 11: Segmentation examples on images affected by
several noise factors. The red points corresponding to the
false iris pixels and the green ones to the false non-iris.
Table 3: Comparative analysis between some recent works,
including the top performing algorithms in the NICE.I con-
test, and the proposed methodology.
Author E
(Tan et al., 2010) 0.0131
(Sankowski et al., 2010) 0.0160 0.0600
(Almeida, 2010) 0.0180
(Tan and Kumar, 2012) 0.0190
Proposed 0.0109 0.0374
dicator that very few iris pixels are classified as non-
iris. This means that almost no useful information
for recognition is lost during the segmentation pro-
cess. The mean FIR value, however, indicates a still
considerable number of noisy pixels that need to be
pruned so as to not present misleading information to
the recognition module. Figure 11 depicts some ex-
amples of segmentation results in images affected by
some of the aforementioned noise factors. One can
easily observe that the great majority of the pixels are
correctly classified.
The use of mutual information from gradient orienta-
tion for centre detection and gradient magnitude for
contour detection presented good results for future
works. Using the extracted iris regions as inputs for
a feature extraction and matching module is the obvi-
ous step to carry on after the segmentation algorithm.
However some improvements can be easily suggested
to the proposed algorithm:
Improve Best Centre/Contour Pair Discrimina-
tion: the current discrimination based on the qual-
ity factor is not the most robust measurement.
Training classifiers using the ρ
, (C) and S(C)
values obtained in the tested dataset will generate
a far more reliable discrimination module.
Noise Detection: as previously referred the ob-
tained results are only promising to a certain ex-
tent. The absence of noise estimation is not ac-
ceptable for integration with a recognition mod-
ule. The number of points that could produce mis-
leading results needs to be significantly reduced in
future works.
Quality Assessment: one question that may be
posed when working with images acquired under
less constrained conditions is if enough informa-
tion is available so as to allow recognition. A
quality assessment module to quantify the amount
of textural information, occlusion and focus of in-
dividual iris images is and important prerequisite
for the application of the proposed algorithm in a
functional iris recognition systems.
Pupil Probability Estimation: In this work we
did not address the pupil segmentation because
of the inherent difficulties presented by the cho-
sen database. We argue that a recognition al-
gorithm with no need of pupillary segmentation
is probably the way forwarded in unconstrained
acquisition settings. However, the same prob-
lem that concerns noise detection is applicable to
pupil localisation: if the pixels corresponding to
this region are not removed from the segmented
iris mask, misleading information will be intro-
duced in the recognition module, resulting in loss
of accuracy. As accurate segmentation is ren-
dered difficult by the intrinsic characteristics of
the UBIRIS.v2 images, estimating a probability
of each pixel belonging to the pupil seems a more
robust way of approaching the problem. Future
works will certainly focus on these three points of
Almeida, P. (2010). A knowledge-based approach to the iris
segmentation problem. Image and Vision Computing,
Chen, R., Lin, X., and Ding, T. (2011). Iris segmentation
for non-cooperative recognition systems. Image Pro-
cessing, 5(5):448 –456.
Chen, Y., Adjouadi, M., Han, C., Wang, J., Barreto, A.,
Rishe, N., and Andrian, J. (2010). A highly accu-
rate and computationally efficient approach for uncon-
strained iris segmentation. Image and Vision Comput-
ing, 28(2):261 – 269.
Daugman, J. (1993). High confidence visual recognition of
persons by a test of statistical independence. IEEE
Transactions on Pattern Analysis and Machine Intel-
ligence, 15(11):1148 –1161.
Daugman, J. (2006). Probing the uniqueness and random-
ness of iriscodes: Results from 200 billion iris pair
comparisons. Proceedings of the IEEE, 94(11):1927–
Daugman, J. (2007). New methods in iris recognition. IEEE
Transactions on Systems, Man, and Cybernetics, Part
B: Cybernetics,, 37(5):1167 –1175.
He, Z., Tan, T., Sun, Z., and Qiu, X. (2009). Toward ac-
curate and fast iris segmentation for iris biometrics.
IEEE Transactions on Pattern Analysis and Machine
Intelligence, 31(9):1670 –1684.
Jain, A., Hong, L., and Pankanti, S. (2000). Biometric iden-
tification. Communications of the ACM, 43(2):90–98.
Kobatake, H. and Hashimoto, S. (1999). Convergence index
filter for vector fields. IEEE Transactions on Image
Processing, 8(8):1029–1038.
Ma, L., Tan, T., Wang, Y., and Zhang, D. (2004). Local in-
tensity variation analysis for iris recognition. Pattern
Recognition, 37(6):1287 – 1298.
Masek, L. (2003). Recognition of Human Iris Patterns for
Biometric Identification. Towards Non-cooperative
Biometric Iris Recognition. PhD thesis.
Nabti, M. and Bouridane, A. (2008). An effective and fast
iris recognition system based on a combined multi-
scale feature extraction technique. Pattern Recogni-
tion, 43(3):868 – 879.
Oliveira, H., Cardoso, J., Magalhaes, A., and Cardoso, M.
(2012). Simultaneous detection of prominent points
on breast cancer conservative treatment images. In
Proceedings of the 19th IEEE International Confer-
ence on Image Processing, pages 2841–2844.
Pawar, M., Lokande, S., and Bapat, V. (2012). Iris segmen-
tation using geodesic active contour for improved tex-
ture extraction in recognition. International Journal
of Computer Applications, 47(16):448–456.
Proenc¸a, H., Filipe, S., Santos, R., Oliveira, J., and Alexan-
dre, L. A. (2010). The ubiris.v2: A database of visi-
ble wavelength iris images captured on-the-move and
at-a-distance. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 32(8):1529 –1535.
Radman, A., Jumari, K., and Zainal, N. (2012). Iris seg-
mentation in visible wavelength environment. Proce-
dia Engineering, 41:743–748.
Ross, A. (2010). Iris recognition: The path forward. Com-
puter, 43(2):30–35.
Sanchez-Avila, C., Sanchez-Reillo, R., and de Martin-
Roche, D. (2002). Iris-based biometric recognition
using dyadic wavelet transform. Aerospace and Elec-
tronic Systems Magazine, IEEE, 17(10):3 – 6.
Sankowski, W., Grabowski, K., Napieralska, M., Zubert,
M., and Napieralski, A. (2010). Reliable algorithm
for iris segmentation in eye image. Image and Vision
Computing, 28(2):231–237.
Shah, S. and Ross, A. (2009). Iris segmentation using
geodesic active contours. IEEE Transactions on In-
formation Forensics and Security,, 4(4):824 –836.
Tan, C. and Kumar, A. (2012). Unified framework for
automated iris segmentation using distantly acquired
face images. IEEE Transactions on Image Process-
ing, 21(9):4068–4079.
Tan, T., He, Z., and Sun, Z. (2010). Efficient and robust seg-
mentation of noisy iris images for non-cooperative iris
recognition. Image and Vision Computing, 28(2):223
– 230.
Vatsa, M., Singh, R., and Noore, A. (2008). Improving iris
recognition performance using segmentation, quality
enhancement, match score fusion, and indexing. IEEE
Transactions on Systems, Man, and Cybernetics, Part
B: Cybernetics,, 38(4):1021 –1035.
Wildes, R. (1997). Iris recognition: an emerging biometric
technology. Proceedings of the IEEE, 85(9):1348
Zuo, J. and Schmid, N. (2010). On a methodology for robust
segmentation of nonideal iris images. IEEE Transac-
tions on Systems, Man, and Cybernetics, Part B: Cy-
bernetics,, 40(3):703 –718.