iris recognition based on hilbert–huang transform

7/28/2019 IRIS RECOGNITION BASED ON HILBERTHUANG TRANSFORM

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Advances in Adaptive Data AnalysisVol. 1, No. 4 (2009) 623641c World Scientific Publishing Company

IRIS RECOGNITION BASED ON

HILBERTHUANG TRANSFORM

ZHIJING YANG, ZHIHUA YANG and LIHUA YANG,

School of Mathematics and Computing Science

Sun Yat-sen University, Guangzhou 510275, China

Information Science School

GuangDong University of Business StudiesGuangzhou 510320, [email protected]

As a reliable approach for human identification, iris recognition has received increasingattention in recent years. This paper proposes a new analysis method for iris recognitionbased on HilbertHuang transform (HHT). We first divide a normalized iris image intoseveral subregions. Then the main frequency center information based on HHT of eachsubregion is employed to form the feature vector. The proposed iris recognition methodhas nice properties, such as translation invariance, scale invariance, rotation invariance,

illumination invariance and robustness to high frequency noise. Moreover, the experi-mental results on the CASIA iris database which is the largest publicly available irisimage data sets show that the performance of the proposed method is encouraging andcomparable to the best iris recognition algorithm found in the current literature.

Keywords : Iris recognition; empirical mode decomposition (EMD); HilbertHuang trans-form (HHT); main frequency center.

1. Introduction

With increasing demands in automated personal identification, biometric authen-tication has been receiving extensive attention over the last decade. Biometrics

employs various physiological or behavioral characteristics, such as fingerprints,

face, iris, retina and palmprints, etc., to accurately identify each individual.13,29

Among these biometric techniques, iris recognition is tested as one of the most

accurate manner of personal identification.4,6,14,22,23

The human iris, a thin circular diaphragm lying between the cornea and the lens,

has an intricate structure and provides many minute characteristics such as furrows,

freckles, crypts, and coronas.2 These visible characteristics, which are generally

called the texture of the iris, are unique to each subject.6,7,14,24 The iris patterns ofthe two eyes of an individual or those of identical twins are completely independent

and uncorrelated. Additionally, the iris is highly stable over a persons lifetime

and lends itself to noninvasive identification since it is an externally visible internal

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624 Z. Yang, Z. Yang & L. Yang

organ. All these desirable properties make iris recognition suitable for highly reliable

personal identification.

For the last decade, a number of researchers have worked on iris recognition and

have achieved great progress. According to the various feature extractions, exist-

ing iris recognition methods can be roughly divided into four major categories: thephase-based methods,6,7,23 the zero-crossing representation-based methods,4,22 the

texture analysis-based methods14,24 and intensity variation analysis methods.15,16

A phase-based method is a process of phase demodulation. Daugman6,7 made use

of multiscale Gabor filters to demodulate texture phase structure information of

the iris. Then the filter outputs were quantized to generate a 2048-bit iriscode

to describe an iris. Tisse et al.23 encoded the instantaneous phase and emergent

frequency with the analytic image (two-dimensional Hilbert transform) as iris fea-

tures. The zero-crossings of wavelet transform provide meaningful information of

image structures. Boles and Boashash4 calculated zero-crossing representation ofone-dimensional wavelet transform at various resolution levels of a virtual circle on

an iris image to characterize the texture of the iris. Wildes et al.24 represented the

iris texture with a Laplacian pyramid constructed with four different resolution lev-

els. Tan et al.14 proposed a well-known texture analysis method by capturing both

global and local details from an iris with the Gabor filters at different scales and

orientations. As an intensity variation analysis method, Tan et al. constructed a set

of one-dimensional intensity signals to contain the most important local variations

of the original two-dimensional iris image. Then the GaussianHermite moments of

such intensity signals are used as distinguishing features.16

Fourier and Wavelet descriptors have been used as powerful tools for feature

extraction which is a crucial processing step for pattern recognition. However, the

main drawback of those methods is that their basis functions are fixed and do not

necessarily match the varying nature of signals. HilbertHuang transform (HHT)

developed by Huang et al. is a new analysis method for nonlinear and nonstation-

ary data.11 It can adaptively decompose any complicated data set into a finite

number of intrinsic mode functions (IMFs) that become the bases representing the

data by empirical mode decomposition (EMD). With Hilbert transform, the IMFsyield instantaneous frequencies as functions of time. The final presentation of the

results is a timefrequencyenergy distribution, designated as the Hilbert spec-

trum that gives sharp identifications of salient information. Therefore, it brings not

only high decomposition efficiency but also sharp frequency and time localizations.

Recently, the HHT has received more attention in terms of interpretations9,18,21 and

applications. Its applications have spread from ocean science,10 biomedicine,12,20

speech signal processing,25 image processing,3 pattern recognition19,26,28 and

so on. Recently, EMD is also used for iris recognition as a low pass filter

in Ref. 5.Since a random iris pattern can be seen as a texture, many well-developed tex-

ture analysis methods have been adapted to recognize the iris.14,24 An iris consists

of some basic elements which are similar to each other and interlaced each other,


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Iris Recognition Based on HilbertHuang Transform 625

OutputHHT LDAIris image NormalizationLocalization

Featureextraction Classification

Iris image preprocessing

Fig. 1. Diagram of the proposed method.

i.e. an iris image is generally periodical to some extent. Therefore the approximate

period is an effective feature for the iris recognition. By employing the main fre-

quency center presented in our previous works26,27 of the Hilbert marginal spectrum

as an approximation for the period of an iris image, a new iris recognition method

based on HHT is proposed in this paper. Unlike directly using the residue of theEMD decomposed iris image for recognition in Ref. 5, the proposed method utilizes

the main frequency center information as the feature vector which is particularly

rotation invariant. In comparison with the existing iris recognition methods, the

proposed algorithm has an excellent percentage of correct classification, and pos-

sesses very nice properties, such as translation invariance, scale invariance, rotation

invariance, illumination invariance and robustness to high frequency noise. Figure 1

illustrates the main steps of our method.

The remainder of this paper is organized as follows. Brief descriptions of image

preprocessing are provided in Sec. 2. A new feature extraction method and matching

are given in Sec. 3. Experimental results and discussions are reported in Sec. 4.

Finally, conclusions of this paper are summarized in Sec. 5.

2. Iris Image Preprocessing

An iris image, contains not only the iris but also some irrelevant parts (e.g. eyelid,

pupil, etc.). A change in the camera-to-eye distance may also result in variations in

the size of the same iris. Therefore, before feature extraction, an iris image needsto be preprocessed to localize and normalize. Since a full description of the prepro-

cessing method is beyond the scope of this paper, such preprocessing is introduced

briefly as follows.

The iris is an annular part between the pupil (inner boundary) and the sclera

(outer boundary). Both the inner boundary and the outer boundary of a typical iris

can approximately be taken as circles. This step detects the inner boundary and

the outer boundary of the iris. Since the localization method proposed in Ref. 14 is

a very effective method, we adopt it here. The main steps are briefly introduced as

follows. Since the pupil is generally darker than its surroundings and its boundaryis a distinct edge feature, it can be found by using edge detection (Canny operator

in experiments). Then a Hough transform is used to find the center and radius of

the pupil. Finally, the outer boundary will be detected by using edge detection and


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(a) (b)

(c)

Fig. 2. Iris image preprocessing: (a) original image; (b) localized image; (c) normalized image.

Hough transform again in a certain region determined by the center of the pupil.

A localized image is shown in Fig. 2(b).Irises from different people may be captured in different sizes and, even for

irises from the same eye, the size may change due to illumination variations and

other factors. It is necessary to compensate for the iris deformation to achieve more

accurate recognition results. Here, we counterclockwise unwrap the iris ring to a

rectangular block with a fixed size (64 512 in our experiments).6,14 That is, the

original iris in a Cartesian coordinate system is projected into a doubly dimen-

sionless pseudopolar coordinate system. The normalization not only reduces to a

certain extent distortion caused by pupil movement but also simplifies subsequent

processing. A normalized image is shown in Fig. 2(c).

3. Feature Extraction and Matching

3.1. The HilbertHuang Transform

The HilbertHuang Transform (HHT) was proposed by Huang et al.,11 which is

an important method for signal processing. It consists of two parts: the empirical

mode decomposition (EMD) and the Hilbert spectrum. With EMD, any complicated

data set can be decomposed into a finite and often small number of intrinsic modefunctions (IMFs). An IMF is defined as a function satisfying the following two

conditions: (1) it has exactly one zero-crossing between any two consecutive local

extrema; (2) it has zero local mean.


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Iris Recognition Based on HilbertHuang Transform 627

By the EMD algorithm, any signal x(t) can be decomposed into finite IMFs,

cj(t) (j = 1, 2, . . . , n), and a residue r(t), where n is the number of IMFs, i.e.

x(t) =n

j=1cj(t) + r(t). (1)

Having obtained the IMFs by EMD, we can apply the Hilbert transform to each

IMF, cj(t), to produce its analytic signal zj(t) = cj(t) + iH[cj(t)] = aj(t)eij(t).

Therefore, x(t) can also be expressed as

x(t) = Renj=1

aj(t)eij(t) + r(t). (2)

Equation (2) enables us to represent the amplitude and the instantaneous frequency

as functions of time in a three-dimensional plot, in which the amplitude is con-toured on the timefrequency plane. The timefrequency distribution of amplitude

is designated as the Hilbert spectrum, denoted by H(f, t) which gives a time

frequencyamplitude distribution of a signal x(t). HHT brings sharp localizations

both in frequency and time domains, so it is very effective for analyzing nonlinear

and nonstationary data.

With the Hilbert spectrum defined, the Hilbert marginal spectrum can be

defined as

h(f) = T0

H(f, t)dt. (3)

The Hilbert marginal spectrum offers a measure of total amplitude (or energy)

contribution from each frequency component.

3.2. Main frequency and main frequency center

It is found that the Hilbert marginal spectrum h(f) has some properties, which can

be used to extract features for iris recognition. Specifically, the main frequency cen-

ter of the Hilbert marginal spectrum can be served as a feature to identify differentirises. The main frequency and main frequency center concepts proposed by us

have been clear described and discussed in our previous works.26,27 We have shown

that the main frequency center can characterize the approximate period very well.

Here, we only review the definitions of main frequency, main frequency center and

other related concepts as follows.

Definition 1 (Main frequency). Let x(t) be an arbitrary time series and h(f)

be its Hilbert marginal spectrum, then fm is called as the main frequency of x(t), if

h(fm) h(f), f.

Definition 2 (Average Hilbert marginal spectrum of signal series). Let

X = {xj(t)|j = 1, 2, . . . , N }, where each xj(t) is a time series, and hj(f) be the


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Hilbert marginal spectrum of xj(t). The average Hilbert marginal spectrum of X

is defined as

H(f) =1

N

N

j=1hj(f). (4)

fHm is called as the average main frequency of X if fHm satisfies H(f

Hm ) H(f), f.

For a given set of signal series, in which signals are approximately periodic, the

main frequency can characterize the approximate period very well. Unfortunately, in

some cases a signal may not have a unique main frequency. To handle this situation,

all the possible main frequencies have to be considered. Therefore, we can utilize

the gravity frequencies, which is called the main frequency center, instead of the

main frequency.

Definition 3 (Main frequency center). Let H(fi) (j = 1, 2, . . . , W ) be the

average Hilbert marginal spectrum of X = {xj(t)|j = 1, 2, . . . , N }. Assume H(fi)

is monotone decreasing respect to i. The main frequency center of X is defined as

fC(X) =

Mi=1 fiH(fi)Mi=1 H(fi)

, (5)

where M is the minimum integer satisfyingM

i=1 H(fi) PW

i=1 H(fi), and 0

iris recognition based on hilbert–huang transform

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