A Faster Method Aiming Iris Extraction

Jeovane Honório Alves

, Gilson Antonio Giraldi

and Luiz Antônio Pereira Neves

Department of Technological and Professional Education, Federal University of Paraná - UFPR, Curitiba, Paraná, Brazil

National Laboratory for Scientific Computing - LNCC, Petrópolis, Brazil

Keywords: Iris Segmentation, Digital Image Processing, Mathematical Morphology.

Abstract: In this paper, we present a technique for iris segmentation. The method finds the pupil in the first step. Next,

it segments the iris using the pupil location. The proposed approach is based on the mathematical

morphology operators of opening and closing, as well as histogram expansion and thresholding. The CASIA

Iris Database from the Institute of Automation of the Chinese Academy of Sciences has been used for the

tests. Several tests were performed with 200 different images, showing the efficiency of the proposed

method.

1 INTRODUCTION

Nowadays, the automatic identification of

individuals is an important application of computer

vision techniques. The iris biometrics is a great

proposal in this area because the complex random

patterns of iris are unique (Daugman, 2004).

Biometrics is a very active research area that

encompasses methods for automatic identification of

individuals. Biometric systems are used in areas

such as companies, banks, shops, universities,

government departments, police department, etc. For

instance, biometrics can be used to identify

criminals and to replace passwords. Despite of its

utility, biometrics still remains expensive. Our

methodology aims to segment the iris using a fast

algorithm and a low cost platform.

There are many works for iris segmentation.

Daugman uses the integro-differential operator to

locate the pupil and the iris region (Daugman, 2004).

The Hough Transform can be used to find circular

objects in the image, and so it has been used to

extract the iris (Borovicka, 2011). Besides, Shah

applied the geodesic active contours to segment the

iris (Shah, 2009).

The paper is organized as follows. In section 2,

the background is explained. In the section 3, the

methodology is described. The section 4 reports the

obtained results. Finally, section 5 presents the

conclusions and further works.

2 MATERIALS AND METHODS

The tests have been performed using the CASIA Iris

Database. Two hundred images of different people

were used for these tests. The tests were done on a

desktop with Intel Core2 Duo E7500 @2.93Ghz,

Microsoft Windows 7 Ultimate, 2GB DDR2 RAM.

We have used the OpenCV 2.1 library (OPENCV,

2011), Code Blocks 10.05 and C++ language. The

techniques used in this paper are well known. They

are based on mathematical morphology operations,

such as opening and closing, the threholding method

and the histogram expansion.

2.1 Mathematical Morphology in Gray

Levels

Two techniques widely used in Mathematical

Morphology in Gray levels are erosion and dilation

operations (Serra, 1982).

Greyscale erosion consists in pass through all

pixels of an image, and in each pixel, check for the

lowest value among it and its neighbours and replace

the value of this pixel by the lowest value. The

neighbours of a pixel are selected according to the

structuring element.

Greyscale dilatation consists in the same idea of

greyscale erosion, but instead of finding the lowest

value, the greyscale dilation seeks for the biggest

one between the pixel and its neighbors.

Erosion and dilatation operations have different

results depending on the structuring element utilized.

Alves J., Giraldi G. and Pereira Neves L..

A Faster Method Aiming Iris Extraction.

DOI: 10.5220/0004303700900093

In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP-2013), pages 90-93

ISBN: 978-989-8565-47-1

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

Structuring element is a set of pixels used to

probe or interact with a given image. In our case, it

is used to define the neighbours to be compared, to

check the lowest value (erosion), and the biggest

value (dilation). For example, in a dilatation or

erosion, if the cross shaped structuring element with

size 3x3 is used, only the current pixel (x, y), the

pixel from above (x, y − 1), the pixel from below (x,

y + 1), the pixel from the left (x − 1, y) and the pixel

on the right (x + 1, y) are scanned.

The verification aims to find the lowest or

biggest value (for erosion and dilation, respectively)

of the pixels of the current region and replace the

current pixel (x, y) by this value. This process is

applied for all pixels in the image. The new value

generated is not used for comparisons in the same

iteration of the morphological operation. Another

area of memory is allocated to save the new values,

and thus the original image is not changed.

The closing and opening operators are based on

the erosion and dilatation techniques. The opening

operator is intended to remove small bright areas

that are surrounded by darker areas. Also, the

closing operator removes small black details that are

surrounded by lighter regions.

2.2 Thresholding

The thresholding is a method for image

segmentation (Gonzalez and Woods, 2007). This

technique is often used when it is necessary to

separate objects from the background. To use this

technique, you must set a value that will be used to

segment the image. This value is called threshold,

which can be obtained by a variety of automatic

techniques, or manually inserted.

One of the most used methods for thresholding is

the Otsu’s method. This method is based on the use

of bimodal histogram (separating the image into two

classes of pixels), splitting the image into two parts

and calculating the best threshold for those parts

where the intra-class variance of them is minimal

(Otsu, 1979). An example of application of the

Otsu’s method, using an image from CASIA

(CASIA,

2011), is represented in Figure 1, where the

original image in gray levels is transformed into a

binary image (Figure 1-b).

Another way to utilize the thresholding method

is to manually set the threshold. An example is

showed in the Figure 1-c, using the value of thirty

(30) for the threshold.

2.3 Histogram Expansion

The Histogram Expansion is a linear transformation

that expands a range of colour levels [xmin , xmax ]

to a range from 0 to ymax (typically 0 to 255). An

adaptation of histogram expansion is mainly used for

images with low contrast. A manually chosen p

value is used to modify the xmin and xmax. The idea

is to compress the ends of the histogram to expand

the center of the histogram. The lower the p value

the greater the intensity of the operation.

(a) (b) (c)

Figure 1: The original image (a), example of Otsu’s Method

(b) and example of manual thresholding (c).

3 METHODOLOGY

Our method aims to find the region of the pupil, and

through it, to segment the iris of the eye. The

method- ology is organized in the following

pipeline: image acquisition, pre-processing and

processing as shown in Figure 2.

Figure 2: The propose pipeline.

3.1 Image Acquisition

The images were acquired from the iris database of

CASIA from ”Institute of Automation of the

Chinese Academy of Sciences.” The version used

was the CA- SIA V3 Interval. The CASIA was

chosen because it is widely used in the literature. We

used 200 images of different people for testing. They

all have the influence of reflection of light in the

pupil, the region where we will focus for the

segmentation of the iris.

The images from CASIA Interval V3 Iris

Database have more details, because they were taken

from a shorter distance and the light is focused on

the pupil, showing the details of the iris.

3.2 Pre-processing

This part is divided in the opening, closing and

AFasterMethodAimingIrisExtraction

histogram expansion operations. The first step is to

perform the gray scale opening. This operation has

the goal to removing small areas that are lighter than

the surrounding areas (see Figure 3-a). We observe

that, the reflection of light present in the pupil is

removed. Fifteen iterations of the morphological

opening operation were performed. The next step is

to apply the morphological closing operator.

The morphological closing operator is used to re-

move small areas that are darker than the

surrounding ones. The idea of this operation is to

increase the gray levels of dark artifacts in the

image. Most of these artifacts are eyelashes. This

operation is executed because the eyelashes are close

to the iris and have almost the same gray level of the

pupil. Thus, the eyelashes are not segmented along

the pupil when the thresholding technique is applied.

Fifteen iterations of the closing operation are

performed as illustrated in Figure 3-b.

Then, a histogram expansion is applied in the

image (Figure 3-c). In this case, the value of p is

zero. This technique is utilized to highlight the pupil

area from other ones in order to get only the pupil

region after the thresholding.

(a) (b) (c)

Figure 3: Image after Opening operation (a), followed by

the Closing operation (b) and the Histogram Expansion

(c).

3.3 Processing

In this section, the processing step is presented.

After the histogram expansion, a thresholding

technique is used. The threshold value is thirty. This

value was chosen after analyzing several images

from CA- SIA. It was found that most of the pixels

of the pupil have gray levels less than or equal to

this value. Moreover, the rest of the image pixels

have a value greater than thirty. Using the

thresholding technique, the pupil is segmented

(black area) from the rest of the image (white area).

The next step is to find the extreme points of the

area of the pupil. The mathematical morphology

operations damaged the region of the pupil.

Therefore, a correction process is necessary.

The idea is to find out these extreme points, and

from these points, creating a circle that replaces the

region of the pupil. To find out these points, the

image is scanned to find the black pixels with the

smallest value of x (left), the lower value of y (up),

the largest value of x (right) and the highest value y

(down).

Using these points, a circle is created at the same

position of the pupil as shown in Figure 4. Further-

more, another circle larger than the previous one is

created, in this case having a radius equal to twenty

pixels. All black pixels of the larger circle, which at

the same position of the smaller circle are white,

possess the same position that the pixels in the inner

region of the iris.

(a) (b) (c)

Figure 4: Application of the Threshold Method (a), fol-

lowed by the creation of small circle (b) and a big circle

(c).

The pixels of the images of the circles are com-

pared with each other. If in a position

(

y), the

pixel of the smaller circle is white and the pixel

from the larger circle is black, the pixel of the

original image with the same position

(

y) is

saved in another image. Thus, only the pixels in

the inner region of the iris are segmented as

illustrated in Figure 5.

(a) (b)

Figure 5: The original image (a) and

iris

segmented

(b).

Only the inner region of the iris is obtained.

4 RESULTS

From the two hundred images tested, twelve (12)

images showed some errors in the segmentation

process. This result shows that our method has an

accuracy rate of 94%.

4.1 Errors

Encounter

Some images did not show the expected result. This

section is designed to show the errors encountered in

the process.

In Figure 6-a, the pupil is very small and has

artifacts due to the reflection. So the thresholding

VISAPP2013-InternationalConferenceonComputerVisionTheoryandApplications

does not properly work, resulting in segmentation

errors (Figure 6-b). The same error occurs in Figure

6-c, where the pupil is also small and has high

influence of reflection of light.

(a) (b)

Figure 6: Original images (a), (c) and the respective

segmented images (b), (d).

Another example is shown in Figure 7-a. Even

with the application of histogram expansion (Figure

7-b), part of the region of the eyelashes is segmented

along the pupil (Figure 7-c) once those pixels have

an intensity value equal or below the threshold. The

result ended up being a defective segmentation

(Figure 7-d).

5 CONCLUSIONS

The tests show that our method has an accuracy rate

of 94%. The images for which the method fails

contain large artifacts due to eyelashes and

reflections. Such artifacts increase the difficulty of

the problem.

The proposed methodology has been developed

for segmentation of iris using a reduced amount of a

priori knowledge. The results show the efficiency of

the technique. Besides, the proposed pipeline runs

very fast in commonly used desktops due to the low

computational cost of the operations involved.

Studies have been carried out in order to apply

the method to identify persons. In further studies we

will compare the proposed technique with other

approaches such as (Shamsi and Kenari, 2009) and

(Wang and Jain, 2003).

(a) (b)

Figure 7: The original image (a). A histogram expansion is

applied at the image (b) and after that, a thresholding tech-

nique (c). Finally, the inner region of the iris is segmented

(d). Unfortunately, this process has failed.

REFERENCES

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CASIA (2011). Institute of Automation of the Chinese

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AFasterMethodAimingIrisExtraction