SHIFT AND ROTATION INVARIANT IRIS FEATURE

EXTRACTION BASED ON NON-SUBSAMPLED

CONTOURLET TRANSFORM AND GLCM

Sirvan Khalighi

, Parisa Tirdad

, Fatemeh Pak

and Urbano Nunes

University of Coimbra, 3030-790 Coimbra, Portugal, and Institute for Systems and Robotics (ISR-UC)

Department of Electrical and Computer Engineering, AZAD University of Qazvin, Qazvin, Iran

Keywords: Gray Level Co-occurrence Matrix, Iris Recognition, Non-Subsampled Contourlet Transform, SVM.

Abstract: A new feature extraction method for iris recognition in non-subsampled contourlet transform (NSCT)

domain is proposed. To extract the features a two-level NSCT, which is a shift-invariant transform, and a

rotation-invariant gray level co-occurrence matrix (GLCM) with 3 different orientations are applied on both

spatial image and NSCT frequency subbands. The extracted feature set is transformed and normalized to

reduce the effect of extreme values in the feature matrix. A set of significant features are selected by using

the minimal redundancy and maximal relevance (mRMR) algorithm. Finally the selected feature set is

classified using support vector machines (SVMs). The classification results using leave one out cross-

validation (LOOCV) on the CASIA iris database, Ver.1 and Ver.4 show that the proposed method performs

at the state-of-the art in the field of iris recognition.

1 INTRODUCTION

Iris recognition is regarded as one of the most

reliable and accurate biometric identification

technologies because of the unique, aging invariant

and non-invasive characteristics of iris. This resulted

in development of a large number of automatic iris

recognition algorithms. Daugman (Daugman, 1993)

first introduced a prototype system for automatic iris

recognition based on multi-scale Gabor wavelets and

extracted the phase information of iris textures.

Wildes (Wildes, 1997) applied a gradient-based

binary edge map and the Hough transform to detect

the iris and pupil boundaries. In (Roy et al., 2011), a

wavelet transform was applied to extract the textural

features and a genetic algorithm was employed to

select the subset of informative features.

Even though, the wavelet transform is popular,

powerful and familiar among the iris processing

techniques, it has its own limitations in capturing

directional information in images such as smooth

contours and the directional edges. This problem is

addressed by Contourlet Transform (CT) (Do and

Vetterli, 2001). In addition to multi-scale and time-

frequency localization properties of wavelets, CT

offers directionality and anisotropy. A 4-level CT

method for iris feature extraction was described in

(Li et al., 2010), in which normalized images are

partitioned into multi-scale and multi-directional

subbands. The normalized energy of subbands are

calculated as features to train a support vector

machine (SVM) classifier. Due to downsampling

and upsampling, the CT lacks shift-invariance. To

overcome this limiting factor, Cunha et al. (Cunha et

al., 2006) proposed a shift-invariant version of CT

designated non-subsampled contourlet transform

(NSCT).

In this paper a new scale, shift and rotation

invariant feature extraction method for iris

recognition in NSCT domain is proposed. After

normalizing the selected regions of interest, some

textural features are extracted from the gray level

co-occurrence matrix (GLCM) of both spatial image

and frequency subbands which resulted from NSCT

decomposition. To improve the recognition rate, the

extracted features are transformed and normalized,

then fed into the minimal redundancy and maximal

relevance (mRMR) feature selection process. Finally

the selected feature set is classified using SVMs.

2 PROPOSED APPROACH

The proposed iris recognition system includes four

470

Khalighi S., Tirdad P., Pak F. and Nunes U. (2012).

SHIFT AND ROTATION INVARIANT IRIS FEATURE EXTRACTION BASED ON NON-SUBSAMPLED CONTOURLET TRANSFORM AND GLCM.

In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, pages 470-475

DOI: 10.5220/0003793904700475

 SciTePress

Figure 1: Overall System Architecture.

major phases: a) iris preprocessing, b) feature

extraction, c) feature transformation and

normalization, and d) feature selection and

classification. Figure 1 shows the architecture of the

system.

2.1 Iris Pre-processing

For the purpose of iris recognition some irrelevant

parts such as eyelid, sclera, eyelashes and pupil

should be removed. In addition, even for the iris of

the same eye, the size may vary depending on

camera-to-eye distance as well as light brightness.

Therefore, the original image needs to be pre-

processed to localize, normalize and enhance the iris

regions, and reduce the influence of the mentioned

factors.

Localization and regions of interest selection: To

locate the inner (iris/pupil) and the outer (iris/sclera)

boundaries, the following steps should be

performed:

1) Reflection removal: Specular reflections

(light spots in the eye image) can cause some

problems in the localization process. To remove the

reflections, the eye image is binarized (using a

threshold = 190). The binarized eye image is then

dilated to consider all possible affected regions.

Then the resulted mask is complemented and applied

to the eye image for marking the reflections spots.

Finally, the detected specular reflections are

“inpainted” (Shah and Ross, 2009) using the 8

surrounding neighbours.

2) Pupillary boundary detection: To detect the

pupillary boundary, the eye image is first binarized

using a threshold value, M+25 (Shah and Ross,

2009) where M is the minimum fixed value of the

inpainted image. In addition to the pupil, other dark

regions of the eye image such as eyelashes fall

below this threshold value. In order to eliminate the

regions corresponded with the eyelashes, a 2-D

median filter with a 10x10 convolution mask is

applied on the binary image. This reduces the

number of candidate regions detected as a

consequence of thresholding (Shah and Ross, 2009).

The remaining regions in the median-filtered binary

image are labelled and the region with the largest

area and the smallest eccentricity is determined as

the pupil region. Finally, the pupil radius and

centroid are calculated by (1) and (2) respectively:

(4 )2pupilRadius A

=×

(1)

(, )( , )

C C xdA A ydA A=

∫∫

(2)

where (C

, C

) denote the center coordinates of the

pupil and A is the area of the pupil.

3) Limbic boundary detection: Before locating

the outer boundary, gamma threshold (Masek et al.,

2003) is adjusted to the iris edge map (extracted by

Canny edge detector) to enhance the iris contrast.

Then the weak edge pixels are set to zero using non-

maxima suppression; thus only the dominant edges

are extracted. Finally, the hysteresis thresholding is

applied to the image. Having the pupil center

coordinates, the radius and centre coordinates of the

iris boundary can be deduced using circular Hough

transform.

To disregard the iris regions occluded by the

eyelid and eyelashes and to avoid loss of

discriminative features, four regions of interest

(ROI) are selected:

I) right side of the iris circle, a sector between

angles –π/4 and π/4 with a radius equal to iris radius

(Figure 2 (a)). II) left side of the iris circle, a sector

between angles 4π/5 and 4π/3 with a radius equal to

iris radius (Figure 2 (a)). III) bottom side of the iris

circle, a sector between angles 4π/3 and –π/4 with a

radius of 1/2 of the iris radius (Figure 2 (b)). IV) a

disk around the pupil with a radius of 1/3 of the iris

radius to cover the pupillary area (Figure 2 (c)).

Normalization and enhancement: To compensate

several external factors such as illumination

variations and imaging distance, the partial iris

images are normalized using “Daugman Rubber

Sheet” model (Daugman, 1993).

Iris Database

Localization and

Region of interest

Normalization

and Enhancement

Test Image

Contrast, Correlation, Energy, Entropy, Dissimilarity,

Sum Average, Sum Variance, Sum of Squares Variance,

Sum Entropy, Difference Variance, Difference Entropy,

Information Measure of Correlation1&2,

Autocorrelation, Cluster Prominence, Cluster Shade,

Maximum Probability, Homogeneity, Inverse

Difference Normalized, Inverse Difference Moment

Normalized, Standard Deviation, Mean, Variance,

Energy of FFT

Feature Selection

Classification

SVM Test

SVM Training

Model

Feature Transformation and

Normalization

mRMR

Pre-processing

Feature Extraction

Result

SHIFT AND ROTATION INVARIANT IRIS FEATURE EXTRACTION BASED ON NON-SUBSAMPLED

CONTOURLET TRANSFORM AND GLCM

471

(a) (b) (c)

Figure 2: Selected regions for normalization.

(a) (b)

Figure 3: (a) Tiled normalized image. (b) Enhanced iris

image by histogram equalization and Wiener filtering.

Since the original iris image has low contrast and

may have non-uniform illumination caused by the

position of the light sources, some enhancements

need to be applied. The histogram equalization is

used to enhance the normalized iris images. The

enhancement involves tessellating the normalized

iris into 32x32 tiles (Figure 3(a)) and subjecting

each tile to histogram equalization. Then for noise-

removal, the Wiener filter is applied to each tile

(Figure 3(b)).

2.2 Feature Extraction

A reliable iris recognition system should extract

features that are invariant to scaling, shift and

rotation. The scale invariance is obtained by

unwrapping the selected iris regions into four fixed

size rectangles. To achieve shift invariance, the

enhanced images are transformed into the frequency

domain using the NSCT which is a shift-invariant

transform and can capture the geometry of the iris

texture. Finally, the GLCM is calculated on both

spatial image and NSCT frequency subbands. The

proposed method is described as follows.

Non-subsampled contourlet transform: In

contourlet transform, the Laplacian Pyramid (LP) is

first used to capture point discontinuities, and then

followed by a Directional Filter Bank (DFB) to link

point discontinuities into linear structures (Po and

Do, 2006). The overall result is an image expansion

using basic elements like contour segments, and thus

called contourlet transform, which is implemented

by a Pyramidal Directional Filter Bank (PDFB) (Do

and Vetterli, 2001). The LP decomposition at each

level generates a down sampled low pass version of

the original image, and the difference between the

original image and the prediction results in a

bandpass image. Due to downsampling and

upsampling presented in both LP and DFB,

contourlet transform is not shift-invariant. The

NSCT is built upon nonsubsampled pyramids and

nonsubsampled directional filter bank (NSDFB);

thus, it is a fully shift-invariant, multi-scale, and

multi-direction image decomposition that has a fast

implementation (Cunha et al., 2006).

Primary features: The enhanced iris image is

decomposed into 6 directions using NSDFB at 2

different scales. Afterward some textural features are

extracted from the spatial iris image and all NSCT

frequency subbands. Textural features mentioned in

Figure 1 are computed on the basis of statistical

distribution of pixels’ intensity at a given position

relative to others in a matrix of pixels called GLCM

(Haralick et al., 1973). Since the GLCM is

computed for different orientations, the rotation of

the iris can be captured by one of the matrices.

Feature extraction based on GLCM is a second-order

statistic that can be employed to analyze an image as

a texture. Although GLCM captures properties of a

texture, it cannot be directly used for further

analysis, such as the comparison of two textures;

thus numeric features which contain significant

information about the textural characteristics are

obtained from the GLCM in three different

directions (Haralick et al., 1973), (Soh et al., 1999)

and (Clausi et al., 2002).

2.3 Feature Transformation and

Normalization

The extracted features are transformed and

normalized in order to reduce the influence of

extreme values. The transformation methods applied

to each feature are described in (Becq et al., 2005).

After a thorough experimental evaluation of each

transform operator over extracted features, it was

empirically verified that the best classification

results were attained with the transform = 1/

√



where Y denotes the feature matrix, and  =





;=1,2,…,and=1,2,…, (where N and

M denote the number of subjects and features

respectively) is the transformed feature matrix.

Thereby this transform was adopted in the overall

iris recognition system. To avoid features in greater

numeric ranges dominating those in smaller numeric

ranges, each feature of the transformed matrix

independently normalized to the (0, 1) range by

applying

π/4

4π/5

4π/3

-π/4 -π/4

4π/3

illar

ICPRAM 2012 - International Conference on Pattern Recognition Applications and Methods

472





=







−





⁄

(3)

where x

is a vector of each independent feature

(Aksoy et al., 2001).

2.4 Feature Selection and Classification

Larger numbers of high-dimensional feature vectors

make the classification process more complex and

less reliable due to features redundancy. To reduce

these effects the mRMR feature selector is used

(Peng et al., 2005).

Support vector machines (SVMs) (Burges, 1998)

are adopted as classifier in this study, given that

neural networks and other classifiers cannot show

reliable classification results in too noisy data.

3 EXPERIMENTAL RESULTS

The performance of the proposed algorithm was

assessed using CASIA iris image databases Ver.1

and Ver.4–Lamp (CASIA Iris Image Database).

CASIA Ver.1 contains a total of 756 grayscale iris

images, from 108 subjects, captured in two sessions

with at least one month interval. CASIA Ver.4-

Lamp was collected using a hand-held iris sensor in

one session. It contains 16213 grayscale iris images

from 411 subjects. CASIA-Ver. 4-Lamp is suitable

for studying problems of non-linear iris

normalization and robust iris feature representation

because of elastic deformation of iris texture due to

pupil expansion and contraction under different

illumination conditions.

In our experiments, a two-level NSCT

decomposition was adopted with 2 and 4 directions

for each pyramidal level respectively. Three GLCMs

were calculated on all NSCT frequency subbands

and the spatial image both in 0

, 90

and 135

. The

normalized iris images were decomposed by the

NSPDFB. We have used “pyrexc” and “pkva” as

NSLP and NSDFB filter in NPDFB decomposition

(Non-subsampled Contourlet Toolbox ver.1.0.0)

given their good performance. SVM-KM toolbox

(SVM and Kernel Method Toolbox) with Gaussian

kernel was used in the classification phase. The

Gaussian kernel degree and C parameters were set to

6 and 100 respectively as suggested by the best

empirical results. Experiments were carried out over

2000 images of 200 randomly selected classes, with

10 images per class and 756 images of 108 classes

for CASIA Ver.4 and Ver.1 respectively.

To estimate the accuracy of classification, leave

one out cross-validation (LOOCV) was used.

Figure 4: Comparison of selecting different iris ROI in the

localization process over the CASIAVer.4. The parameters

of method I are Ө = (0,2π), r = IrisR, method II Ө = (0,2π),

r = 1/3×IrisR and method III are Ө

Left

= (3π/4, 5π/4), Ө

Right

= (-π/4, π/4), r = IrisR.

Receiver operating characteristics (ROC) in

Figure 4 show the comparison of different iris

localization approaches on the Ver.4. Each curve is

denoted by symbols r, Ө which represent normalized

polar coordinates. Ө = (0,2π), r = IrisR refers to a

disk around the iris with iris radius, which covers the

whole iris region. Ө = (0,2π), r = 1/3×IrisR refers to

a disk around the iris with 1/3 iris radius, similar to

Figure 2 (c). Ө

Left

= (3π/4, 5π/4), Ө

Right

= (-π/4, π/4),

r = IrisR refers to a state similar to Figure 2 (a). The

results illustrate the superior performance of the

proposed approach over the other mentioned

methods in Figure 4.

From the feature extraction process, a total of

2048 features for each sample resulted. The

transformed and normalized feature matrix was fed

into the feature selection method. The number of

selected features based on the mRMR results, was

set to 357 because it provided the best mean

accuracy in a grid search. The most effective

features were correlation and homogeneity and the

least significant was maximum probability feature in

the extracted group (Figure 1). From each ROI, the

following numbers of features were selected: 68, 70,

125, 94 which correspond to Figure 2 (a) left, (a)

right, (b) and (c) respectively. These results

demonstrate that the region between (4π/3,–π/4) with

1/2 of iris radius contains more relevant features

than other regions.

Figure 5 shows the comparison of the proposed

feature extraction method using NSCT, contourlet

0 0.1 0.2 0.3 0.4 0.5 0.

0.9

0.92

0.94

0.96

0.98

False Positive Rate

Genuine Acceptance Rate

Method I

Method II

Method III

Proposed Method

SHIFT AND ROTATION INVARIANT IRIS FEATURE EXTRACTION BASED ON NON-SUBSAMPLED

CONTOURLET TRANSFORM AND GLCM

473

Figure 5: Performance of different feature extraction

methods over the CASIAVer.4.

and wavelet transforms. This diagram shows the

highest accuracy obtained by NSCT due to its

redundant structure.

Figure 6: Comparison of classification accuracy for

different frequency transformation with different numbers

of features over the CASIAVer.4.

Maximum classification accuracies of NSCT

with two different kernels, contourlet and wavelet

transforms with different number of features, are

shown in Figure 6. For the wavelet transform the

best mean accuracy of 93.40% was obtained for 322

features; however this accuracy is lower than the

case of using NSCT. For the contourlet transform

the best mean accuracy

of 96.55% was attained for

490 features, which in comparison with NSCT has a

Table 1: Performance comparison of some popular

algorithms on CASIA database Ver. 1.

Methodology

Feature Extraction

methods

Accuracy

Rate %

Daugman

(Daugman, 1993)

Gabor wavelets 100

Qi M. et al.

(Qi M. et al, 2008)

Gabor filter 99.92

Chen et al.

(Chen et al., 2009)

1-D circular profile 99.35

Poursaberi et al.

(Poursaberi et al., 2007)

wavelet

Daubechies2

99.31

Our approach without

LOOCV

NSCT and GLCM 100

Our approach by

LOOCV

(mean accuracy)

NSCT and GLCM 98.29

higher number of features. This diagram

corroborates the high performance of our approach

over the CASIA Ver.4

with the average and

maximum accuracies of 96.55% and 100%,

respectively. Furthermore, the comparison results of

the classification accurac

y over the CASIAVer.1 are

as follow. The best mean accuracy of 91.40% with

185 features and 97.35% with 565 features were

achieved for the wavelet and contourlet transforms,

respectively. The best mean accuracy of 98.29% was

achieved with the NSCT using 424 features.

Considering that there isn’t any reported result

based on CASIA Ver. 4 lamp, the proposed method

was compared with state of the art methods, just

with CASIA Ver. 1. Table 1 shows the comparison

results of the proposed method with the others.

Some accuracy results are higher than our mean

accuracy, however we highlight that our reported

results were obtained using the LOOCV method in

the testing process.

4 CONCLUSIONS

A new feature extraction method for iris recognition

based on NSCT was presented. The described

technique has some advantages over other

techniques. First, this method selects four ROIs to

make use of the most significant information in the

iris texture. Second, the extracted features are

invariant to scaling, shift and rotation, which are

important properties in the iris recognition. Third, to

reduce the effect of extreme values in the feature

matrix the extracted feature set was transformed and

normalized, which remarkably improved the

recognition rate. Fourth, mRMR was employed as a

feature selector which has proven to be one of the

most powerful and stable among known feature

0 0.02 0.04 0.06 0.08 0.1

0.3

0.4

0.5

0.6

0.7

0.8

0.9

False Positive Rate

Genuine Acceptance Rate

NSCT

Contourlet

wavelet

0 100 200 300 400 500 600

100

Number of features

Accuracy%

NSCT(gaussian kernel)

NSCT(Poly kernel)

contourlet(gaussian kernel)

wavelet(gaussian kernel)

150 200 250 300 350 400

100

ICPRAM 2012 - International Conference on Pattern Recognition Applications and Methods

474

selectors. Finally, to estimate the mean accuracy of

the proposed method LOOCV was used. The

obtained average accuracies on CASIA Ver.4 and

Ver.1 were 96.55% and 98.29% respectively, and

the accuracies without using LOOCV for both

datasets were 100% which empirically illustrate the

reliability and effectiveness of the presented method.

ACKNOWLEDGEMENTS

This work has been partially supported by the

QREN funded project SLEEPTIGHT, with FEDER

reference CENTRO-01-0202-FEDER-011530.

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SHIFT AND ROTATION INVARIANT IRIS FEATURE EXTRACTION BASED ON NON-SUBSAMPLED

CONTOURLET TRANSFORM AND GLCM

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