Detection of Iris in Images Using Bright

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ISSN 10546618, Pattern Recognition and Image Analysis, 2011, Vol. 21, No. 1, pp. 41–44. © Pleiades Publishing, Ltd., 2011.

INTRODUCTION

Recognition of human by iris is one of the mostdemanded biometric technologies. Algorithm of irissize and position estimation is an essential part of irisregistration systems. The following characteristics of algorithm are important: performance (difference

between real and detected iris coordinates should besmall); reliability (algorithm should be able to detectiris in images where it really presents and reject images

without iris); throughput (in order to be used in real world iris input systems, the algorithm should be capa ble to process video input stream of the system in realtime and by ordinary computing means); robustnessagainst noises and obstacles (like parasite reflections

and occlusions by eyelids and eyelashes); ability toprocess images obtained by various sensors and in various environment conditions (one of such requirements here is an ability to detect irises differing in size

by several times).

Since outer borders for both pupil and iris can beapproximated by circles with good precision, circledetection is a central element of any system of irisdetection in image. There are plenty of methods of circle (or circumference) detection implemented andtested for this task: detecting of mass center of anobject selected by thresholding function [1], search for the point being the most distant from object bound

aries [2], maximization of an integral differentialoperator with a circular symmetry [3], generalized [4,5, 14] and split Hough transform [6], Hough transform using brightness gradient [7, 8], method of

brightness gradient projections [9], paired gradient vectors [10], circular shortest path construction [11],and restoration of circles on the base of sets of ran

domly chosen points [12]. However, none of these

methods satisfies all the conditions listed above.Thresholding and image morphology methods are fast

but fail if specular reflection is present inside pupil. Onthe other hand, generalized Hough transforms andDaugman’s operator give stable results but requiremuch computation and thus are inadequate for realtime applications. More elaborated recent methods[10–12] result in better rate accuracy/speed, but eventhey do not satisfy the whole range of conditions.

However, up to the moment, none of the methodshas accounted for the fact that there are two circles inan iris image (namely, the pupil–iris border and theiris–sclera border), and the parameters of these circles

are interrelated. Simultaneous search for both thepupil and iris as circles, the parameters of which satisfy certain mutual restrictions set by the nature of the iris,makes it possible to significantly improve the algorithm characteristics as compared to the search for asingle circle. We present an algorithm of searching for an iris based on the constructing of histograms, or pro

jections of local brightness gradients, and comparingof their maxima being regarded as possible locations of pupil and iris borders.

CONSTRUCTION OF CIRCULARPROJECTIONS OF BRIGHTNESS GRADIENT

As in most recognition problems, the main goal isreduced to choosing the best among feasible alternatives, particularly, the best location of the two circles of the pupil and the iris. To construct the feasible locations of the pupil and the iris, method of projections(histograms) is enployed, and circular projections of local gradients of the image brightness are used. Thecircular projections were centered with regard to thelocation of the specified point c of the approximatelocation of the eye center. The input data are a bitmap

Detection of Iris in Images

Using Brightness Gradient Projections

I. A. Matveev

Dorodnicyn Computing Center, Russian Academy of Sciences, ul. Vavilova 40, Moscow, 119333 Russiaemail: [email protected]

Abstract—A method is proposed to detect human iris location and size in digital image given some pointlying inside the pupil. The method is based on construction of histograms, or projections of local brightnessgradients, and combinations of projections' maxima being regarded as possible locations of pupil and iris borders. The method is notable for its low computational complexity and high tolerance to noise.

Keywords: iris recognition, image processing, brightness gradient, method of projections.

DOI: 10.1134/S105466181101010X

Received October 20, 2010

PROCEEDINGS OF IMTA32010


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PATTERN RECOGNITION AND IMAGE ANALYSIS Vol. 21 No. 1 2011

MATVEEV

monochrome image and approximate coordinates of eye center as detected by [1], [9] or some other method. The projections are constructed relative tothe eye center. Irises with any size fitting into an imageshould be detected, and the iris should not span

strongly out of the image frame. Thus a length of pro jection spanning in a certain direction is limited by theimage border. And since iris diameter cannot exceedimage size (i.e. minimum of image width and imageheight) the projection length is limited by half of animage size. Denote approximate center position as:

c = (c x , c y)T . Method [9] guarantees its distance to real

pupil center is not greater than half of pupil’s radius.For simplicity consider this point as a coordinate origin. Let us also specify the following designations: x =

( x , y)T for the coordinate vector of an image point,b( x) for the image brightness (intensity) in this point,and g( x) = ∇b( x) for the brightness gradient. The gra

dient in a digitized discrete image is calculated usingSobel mask.

Only points with certain gradient value and direction can belong to iris border. The set of these point isgiven by indicator function:

(1)v U x( ) =1, if g T 1 and

x g⋅ x g T 2 and U > >

0, otherwise,

where T 1 and T 2 are thresholds, U is an auxiliary condition specifying a plane sector (quadrant) with regardto the coordinate origin (i.e., the point c). T 1 is speci

fied in such a way as to reject gradients generated by

noise; the value of T 1 is 6 max{σ, 2}, where σ is the brightness dispersion caused by noise. T 2 is set to

include only points where brightness gradient has

approximately same direction as the point vector: T 2 isarccos(π/6). The following conditions are used toselect left, right, lower, and upper quadrants:

(2)

Taking one of the above conditions, for specification of v U ( x), for instance, U ≡ R, it is possible toobtain a histogram of number of points satisfying the

conditions as a function of radius. This histogram isreferred to as gradient projection. Here is an exampleof a normalized histogram with reference to the radius(tangent projection) of the number of points for thegradient of a desired direction and value in the rightcircle part:

(3)

DETECTION OF CIRCLES FROMPROJECTION MAXIMA

A projection may have several local maxima.Denote the value of the projection in the nth local

maximum as (r ) and the location of the

maximum as (r ). The figure presents a

picture of an eye and a projection ΠR(r ) constructedon the basis of the image for the right quadrant. The

locations of eight local maxima (r ), n =

1, …, 8 are indicated in the histogram. After detectinglocal maxima positions for all four quadrants distancesto hypothetical circle borders from central point in

appropriate direction can be estimated.The combination of these values provides means of

finding the center coordinates q = (q x , q y)T of different

hypothetical circles. (For the sake of simplicity,assume the location of the nth local minimum of the

U projection to be P U (n) – (r ).)

(4)

2

U

L: x y , x 0,

R: x y , x 0,> >

B : x y , x 0,< <

T : x y , x 0.> >

=

ΠR r ( ) 1

2πr v R x( ).

r 0.5– x r 0.5+< <

∑=

loc ΠRn r ,

max

loc ΠRn r ,

max arg

loc ΠRn r ,

max arg

loc ΠU n r ,

max arg

q x n m, 1

2 P R n( ) P L m( )–( ),=

C

1 2 3 4 5 6 7 8

ΠR(r )

An example of a circle projection and locations of localmaxima.


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DETECTION OF IRIS IN IMAGES 43

(5)

The coordinates of the circle centers are calculated asfor the mean location (half–sum) of two local maximalocations, and the radii are assumed to be the half–difference of the two maxima. There are two radius evaluations, in vertical and horizontal direction:

(6)

The consolidated circle radius is considered as theaverage value of the two estimates:

(7)

At the same time the ratio between the two estimatesmight be regarded as an indicator of the quality of acircle:

(8)

Quality of circle obtained for four given positions of local maxima (n, m, u, v ) (in right, left, top and bottom projections respectively) may be estimated as sumof projection function values in these positions:

(9)

SELECTION OF INTERRELATED CIRCLES

So, various hypothetical iris and pupil circles areconstructed with the use of the method of circle pro

jections. These circles might be pupils (index P ) andirises (index I ). A circle is specified by the location of the center and its radius, (qP , r P ) and (qI , r I ). Theseparameters are restricted due to the nature of a humaniris [13]:

• r P > r I (iris radius cannot be six times larger than

the pupil radius);

• r P < r I (the pupil cannot be more than 75% of

the iris);

• d < r P , d = (the iris center lies inside thepupil);

• 2(r I – r p – d ) > r I – r P + d or d < in a

reduced view (the ratio between the minimal and maximal width of the iris is less than or equal to 2).

q yu v , 1

2 P T u( ) P B v ( )–( ).=

ρ x n m,

12 P R n( ) P L m( )+( ),=

ρ yu v , 1

2 P T u( ) P B v ( )+( ).=

ρn m u v , , , 12 ρ x

n m, ρ yu v ,

+( ).=

θ ρ x ρ y,( ) 1 ρ x ρ y–ρ x ρ y+.–=

Q n m u v , , , θ ρ x n m, ρ y

u v ,,( )=

×loc ΠR r ( )

n r ,

max loc ΠL r ( )m r ,

max +

+ loc ΠT r ( )u r ,

max loc ΠB r ( )v r ,

max +

.

1

6

3

4

qP qI –

r I r P –

3

Among all the pairs of circles satisfying the restrictions, let us choose the one with the maximal sum of quality indicators.

ESTIMATION AND EXPERIMENTAL VALIDATION

Thus, the algorithm in whole consists of the following three steps:

(1) Calculation of local gradients in image. Each of two gradient components require six memory reads,five additions, one subtraction, three bit shifts and onememory write, that is totally 24 integer operations per one image point.

(2) Construction of circle projections of a gradient(histograms of density for points with specific characteristics depending on distances) in the four quadrantsand detection of the local minimum location. Calculation of the indicator function (1) is required. The

check of the condition > T 1 requires two readingsof the values g x and g y, two multiplications, one summation, and one comparison, which are six operationsin total. For most image points, the brightness gradientis not big and does not pass the check, and such pointsare not processed further. The check of the condition

T 2 < is divided into the check of x ⋅ g > 0 and

the check of ( x ⋅ g)2 > x2g2, in order to avoid calculation of the square root. The first check requires six operations and rejects half of the points. The secondrequires four extra multiplications and a comparison.The complete processing of a point on the steprequires from six to eighteen operations, and mostpoints are processed in six operations.

(3) Enumeration of circle combinations searchingmost likely (with biggest quality) pair. The computational cost of this step depends on the number of localmaxima selected rather than on source image size.Typical number of local maxima is around ten, andthere could be dosens of thousands of combinationsfrom four maxima positions, however most of them donot pass the limitations. Only less than hundredhypotheses are sensible and require quality calculationand comparison. Hence the amount of calculations inthis step is negligible compared to the previous ones.

Totally, from 30 to 42 integer operations per oneimage pixel are required, and majority of pixels aretreated with 30 operations. Image of the typical size of 640 × 480 pixels is processed in 10 million operations.

The algorithm was tested with the following data bases:

UBIRIS (http://www.di.ubi.pt/hugomcp/doc/ubiris.pdf), 1207 images;

g

x g⋅ x g

T 22


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MATVEEV

CASIA Iris Image Database (http://www.sinobiometrics.com), 16 213 images;

IRIS Challenge Evaluation (http://iris.nist.gov/ice/), 2954 images.

Every image was of the size 640 × 480 pixels; irisradii lay in the range from 50 to 200 pixels. The ratio of the pupil radius to the iris radius was in the range from0.2 to 0.75.

Testing technique. Eye images were viewed by ahuman expert who determined positions and sizes of

iris and pupil circles in each image. These data werethen considered as “groundtruth” and were used for method verification. Then images were processedautomatically. The method [9] was used to detect thepoint inside pupil. Then the method presented in thisarticle was used to find the pupil and iris parameters,and the obtained values were compared to the onesthat were previously specified by the expert. The tableexposes the number of gross (the difference in at leastone of the parameters is more than 10%) and moderate (the difference is from 5 to 10%) errors. When thedifference in parameters was less than 5%, the iris wasassumed to be the correct result of processing.

Execution of the algorithm takes not more than

0.01 s in PC with PIV 3 GHz CPU for an image of 640 × 480 pixels. Main share of calculation time istaken by Sobel gradient estimation.

The proposed method of iris detection might beused for the preliminary search for coordinates of theiris (with the accuracy up to 5%) and pupil circles(with the accuracy up to 10%) as far as there is a pointknown to lie within the pupil. The method can be runin real time.

REFERENCES

1. T. Maenpaa, “An Iterative Algorithm for Fast Iris

Detection,” in Proc. IWBRS 2005 (Beijing Press,Beijing), pp. 127–134.

2. B. Lipinski, Iris Recognition: Detecting the Pupil, Available from: http//cnx.org/content/m12487/latest/.

3. J. Daugman, “High Confidence Personal Identification by Rapid Video Analysis of Iris Texture,” in Proc.IEEE Int. Carnahan Conf. on Security Technology(Madrid, 1992), pp. 50–60.

4. R. P. Wildes, J. C. Asmuth, G. L. Green, et al., “A System for Automated Iris Recognition,” in Proc. 2nd

IEEE Workshop on Applications of Computer Vision(Sarasota, FL, 1994), pp. 121–128.

5. R. O. Duda and P. E. Hart, “Use of the Hough Transformation to Detect Lines and Curves in Pictures,”Comm. ACM 15, 11–15 (1972).

6. D. E. Ben, M. S. Nixon, and J. N. Carter, “Robust EyeCentre Extraction Using the Hough Transform,” inProc. 1st Int. Conf. on Audio– and Video–Based Biometric Person Authentication (Crans–Montana, 1997).

7. C. Kimme, D. Dallard, and J. Sklansky, “Finding Circles by Array of Accumulators,” Comm. ACM 18,

120–122 (1975).

8. E. R. Davies, “A High Speed Algorithm for Circular Object Location,” Patten Recogn. Lett. 6, 323–333(1987).

9. I. A. Matveev, “Method of Detection of Circle withKnown Internal Point,” Trans. Inst. Syst. Anal. Russ.

Acad. Sci. Dynam. Heterogeneous Syst. 31 (1), 288–293 (2007).

10. A. A. Rad, K. Faez, and N. Qaragozlou, “Fast Detection Using Gradient Pair Vectors,” in Proc. VII Digital Image Computing: Techniques and Applications.Dec. 10–12, 2003, Sydney, Ed. by C. Sun, H. Talbot,S. Oerselin, and T. Adriaansen (Sydney, 2003).

11. C. Sun and S. Pallottino, “Circular Shortest Path inImages,” Pattern Recogn. 36 (3), 709–719 (2003).

12. T.C. Chen and K.L. Chung, “An Efficient Randomized Algorithm for Detecting Circles,” Comput. VisionImage Understand. 83, 172–191 (2001).

13. J. J. Kansky, Clinical Ophthalmology: A Systematic Approach, 5th ed. (Elsevier, London, 2003).

14. L. Masek and P. Kovesi, MATLAB Source Code for aBiometric Identification System Based on Iris Patterns(The School of Computer Science and Software Engineering, The University of Western Australia, 2003).

Results of applying the algorithm to images from different image test databases

Database Number of imagesNumber

of moderate errorsof pupil detection

Number of gross errors

of pupil detection

Number of moderate errors

of iris detection

Number of gross errors

of iris detection

UBIRIS 1201 296 3 31 1

CASIA 16213 2070 48 274 43

ICE 2954 116 17 18 5

Ivan Matveev. Master’s degree atthe Moscow Institute of Physics andTechnology in 1997. In 1999 PhD inapplied mathematics, Computer Centre, Russian Academy of Sciences. Since 2004 head of the Intellectual Systems sector of the Complex Systems department, Computer Centre, Russian Academy of Sciences. Research interests: biometricidentification, face and iris recognition, and realtime image processing.

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Detection of Iris in Images Using Bright