iris recognition of defocused images for mobile phones

July 18, 2012 17:35 WSPC/INSTRUCTION FILE ws-ijprai

International Journal of Pattern Recognition and Artificial Intelligencec© World Scientific Publishing Company

IRIS RECOGNITION OF DEFOCUSED IMAGES

FOR MOBILE PHONES

BO LIU†‡’1, SIEW-KEI LAM‡,2,THAMBIPILLAI SRIKANTHAN‡’3 and WEIQI YUAN†’4

†Computer Vision Group, Shenyang University of Technology

Shenyang, China, 110870

‡Centre for High Performance Embedded Systems

Nanyang Technological University

Singapore, 637553

[email protected]@[email protected]

[email protected]

In this paper, we introduce a novel iris recognition approach for mobile phones, whichtakes into account imaging noise arising from image capture outside the Depth of Field(DOF) of cameras. Unlike existing approaches that rely on special hardware to extendthe DOF or computationally expensive algorithms to restore the defocused images priorto recognition, the proposed method performs recognition on the defocused images basedon the stable bits in the iris code representation that are robust to imaging noise. Tothe best of our knowledge, our work is the first to investigate the characteristics of irisfeatures for varying degree of image defocus when the images are captured outside theDOF of cameras. Based on our findings, we present a method to determine the stablebits of an enrolled image. When compared to iris recognition of defocused images thatrelies on the entire code representation, the proposed recognition method increases theinter-class variability while reducing the intra-class variability of the samples considered.This leads to smaller intersections between the intra-class and inter-class distance dis-tributions, which results in higher recognition performance. Experimental results basedon over 15,000 images show that the proposed method achieves an average recognitionperformance gain of about 2 times. It is envisioned that the proposed method can beincorporated as part of a multi-biometric system for mobile phones due to its lightweightcomputational requirements, which is well suited for power sensitive solutions.

Keywords: Iris Recognition; Defocused Iris Image; Depth of Field.

1. Introduction

The prevalence of internet-enabled mobile phones have given rise to many applica-

tions such as mobile banking, contactless payment, mobile marketing, etc., which

requires tight security protection of user information. As such, traditional security

features on mobile phones such as personal identification numbers is expected make

way for more sophisticated biometric systems. One of the most promising biometric

1


2 B. LIU, S. K. LAM, T. SRIKANTHAN & W. YUAN

authentication systems for mobile phones is iris recognition, which has been shown

to be an effective method for recognizing individuals. In addition, iris recognition

can be readily adopted in mobile phones due to the availability of built-in cameras

in mobile phones today.7,24,32

Most iris recognition methods adopt a similar strategy to the well-known Daug-

man’s approach, which employ binary coding of phase information to represent

the iris features.8−11 The phase information is obtained by applying mathematical

transformation (texture filtering) onto the iris images.1,4,8−11,25,27,42 For example,

Daugman extracts the iris texture’s local phase feature using Gabor filtering.8−11

The work in Refs. 42 extracts features by applying four levels of processing onto

the iris images using the Laplace pyramid decomposition algorithm. In Refs.

4, Boles and his group extract the ‘zero-crossings’ information using 1-D cubic

spline wavelet. Ma makes use of the dyadic wavelet method to extract the binary

features.27 In Refs. 25, Lim and his group extract the iris images’ high frequency

information as features by adopting the two-dimensional Harr wavelet transforma-

tion. Azizi makes use of the contourlet wavelet transformation to obtain the iris

images’ intrinsic texture structure and then choose the optimal feature sequences.1

While all of the above methods can achieve good recognition performance, they are

unable to work with images that are affected by imaging noise introduced when the

images are captured in non-constrained environment.

One of the key problems with iris image acquisition in mobile phones is the

limited DOF of the camera system. Iris camera systems must be capable of cap-

turing the iris’s texture details with high camera shutter speed so that the eye is

not exposed to long period of lighting. In order to achieve this, the camera system

requires a long focus lens to magnify the iris, and a high numerical aperture to

enable sufficient lighting.3,8−11,28,36 Such camera systems have short DOF, which

increases the difficulty in the usage of the iris recognition system by untrained and

non-cooperative users as images captured outside the DOF of cameras lead to low

recognition performance.30 This problem arises as iris patterns of images captured

outside the DOF are transformed by blurring caused by optical defocusing.21 Us-

ing an auto-focusing camera to overcome this problem will lead to increase in the

cost, size, and complexity of the system.15,21 In addition, techniques based on the

use of audio/visual cues to guide the user to appropriate distance from camera

during image acquisition is not practical to the mobile phone user, as it can be

time-consuming and will therefore hinder the acceptance of the process in daily

use.16,40

Previous work on extending the DOF of iris recognition system can be catego-

rized into two main approaches: 1) using special hardware such as aspheric optics

and 2) applying image restoration on the defocused images prior to recognition. The

former approach employs specially designed aspheric optics to increase the focus

invariance along the axis of the lens, while the latter uses mathematical models that

are constructed based on certain image capture conditions to restore the defocused

images. These two approaches have a common aim to enhance the quality of the


Iris Recognition of Defocused Images for Mobile Phones 3

captured images in order to improve the recognition performance for robust iris

recognition systems.

In the first approach, computational imaging systems that commonly em-

ploy wave-front coded techniques (originally proposed by Dowski and Cathey) are

used.6,12 These systems introduce a cubic phase mask in the standard optical sys-

tem to produce large focus invariance and employ signal processing algorithms on

the captured images. Various wave-front coded lenses have been proposed for iris

recognition, which take into account the non-linear steps required for iris feature

extraction and identification.29,30,33 The work in Refs. 36 and 40 examined the uti-

lization of wave-front coded imaging system for increasing the operational range of

iris recognition by considering the absence and presence of post-processing meth-

ods to restore the optical defocus in images that are introduced by the aspheric

optic. They have demonstrated that the use of such optical elements is capable of

relaxing the DOF requirements without the need to restore the defocused images.

The work in Refs. 2 and 3 proposed a pattern matching strategy for iris recogni-

tion of defocused images based on correlation filtering. However, this method can

only be used for images captured with wave-front coded systems. The methods dis-

cussed above require special optical element insertion, large computations and in

some cases generate images with lower Signal-to-Noise Ratio (SNR), which must

undergo post-processing prior to recognition.36

The second approach aims to construct a mathematic model for restoring the

defocused images prior to iris recognition. A real-time iris image restoration method

based on inverse filtering was introduced in Refs. 22 and 23. However, this method

did not consider noise factors. In addition, the point spread function (PSF) was

heuristically determined without taking into consideration the camera optics, and

this resulted in recognition performance degradation. A non-blind de-convolution

algorithm to restore iris images and extend the DOF was proposed in Ref. 21. The

authors estimated the PSF using the focus score that was measured from the high

frequency components of the iris regions after irrelevant objects such as eyelashes

and eyelids were removed. However, the Gaussian smoothness term used in the

algorithm tend to over-smooth the restored image. An iris restoration algorithm

using more accurate information pertaining to the image capture conditions that

are computed from the iris images was proposed in Ref. 20, which achieves better

performance on both synthetic and real data set when compared with the state-of-

art iris restoration algorithm. While some of the image restoration methods have

shown promising results, the biggest challenge in these approaches lie in obtaining

suitable information pertaining to the image capture conditions that is required to

construct the mathematic model for restoring the defocused images.20 In addition,

similar to the first approach, techniques relying on image restoration algorithms

have high computational complexity and do not lend themselves well towards the

power sensitive requirements of mobile phones.

The method proposed in this paper does not require special hardware or image

restoration algorithms for iris recognition of defocused images that are captured



outside the camera DOF. The proposed method is based on our investigation which

reveals that certain iris features are not sensitive to optical defocusing and these

features can be represented as stable bits in the iris code representation. In addition,

we will also present our analysis of the iris feature characteristics in varying degree

of image defocus. Based on these findings, we will propose a method to identify the

stable bits of a given enrolled image, and show that good recognition performance

can be achieved by limiting the pattern matching to these stable bits instead of

using the entire iris code representation. As only a single enrolled image is required

to obtain the stable bits, the proposed approach is highly practical. In this paper, we

assume that the enrolled image used in the proposed method is a clear image that

is captured within the camera DOF. Finally, the proposed method is well suited to

be incorporated as part of a multi-biometric system for mobile phones as it does

not require any power hungry computations as compared to existing approaches for

extending the DOF of iris recognition system.

While previous work Refs. 5, 13, 14, 17-19, 35, 38 and 41 have reported on the

prospects of using stable iris features for recognition, to the best of our knowledge,

this work is the first to investigate the characteristics of iris features in varying

degree of image defocus when the images are captured outside the DOF of cameras.

Our findings have led to the development of the proposed method that is capable

of recognizing defocused iris images without the need for special hardware or time

consuming image restoration algorithms.

The paper is organized as follows. In the following section, we will discuss our

study, which reveals that certain iris features are not sensitive to optical defocusing

and these features can be represented as stable bits in the iris code representation.

These stable bits can then be exploited in the recognition process. In Sec. 3, we will

present an efficient method to identify stable bits from a single enrolled image based

on our analysis of the iris feature characteristics in varying degree of image defocus.

Next, we present experimental results to show that the proposed method can lead

to significantly higher recognition performance when compared to iris recognition

of defocused images that relies on the entire code representation. Sec. 5 concludes

the paper with a discussion on future work.

2. Exploiting Stable Features in Defocus Images for Iris

Recognition

In this section, we will discuss our experimental setup and analysis which reveals

that certain iris features in images that are captured outside the camera DOF are

invariant to optical defocusing. We will present a simple method to identify these

features and show that they can be used for iris recognition.

2.1. Image acquisition

In order to obtain an iris image dataset for our study, we employed an iris image

sampling system that is controlled by a stepping motor. The system is capable of



capturing a sequence of 61 iris images with a sampling interval of 2mm for a single

eye. The sequence of 61 images consists of a single clear image (captured in sharp

focus within the camera DOF), while the remaining images may consist of different

degree of defocus as they are captured outside the camera DOF at varying distance

from the camera. In order to identify the clear image from a sequence, we employ

the clarity evaluation function presented in Refs. 26, 31, 34 and 45. We then selected

15 images (which are subjected to optical defocus) before and after the clear image

to be included in the dataset. Fig. 1 shows an example of an iris image sequence,

where the 16th image is in sharp focus as it is captured within the camera DOF.

The 30 images captured before and after the clear image are subjected to varying

degree of defocusing. The resolution of each image is 640×480 pixels.

Fig. 2 shows the clarity evaluation function curve of a particular sequence of iris

images. The x-axis denotes the image number in the sequence, which corresponds

to the sampling distance. The first half of the curve shows the gradual decrease in

defocus from the 1st image to the 16th image, while the second half of the curve

shows the gradual increase in defocus of the remaining images. As the sampling

process of a sequence is performed with a fixed interval (i.e. 2mm apart), we can

represent the degree of defocus using the image number in the sequence.

For this study, we have selected 50 people, where each of their left and right eyes

are sampled 5 times using the iris image sampling system. Each sampling sequence

of the same eye may differ due to lighting conditions, position of the iris during

Fig. 1. A sequence of iris images.



5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Image number

Nor

mal

ized

val

ue o

f cla

rity

eval

uatio

n fu

ntio

n

Fig. 2. Clarity evaluation function curve of a sequence of iris images.

image capture, etc. Incorporating such variations during image acquisition of a

single eye enable us to obtain a dataset that can provide more realistic evaluations.

Hence, our dataset consists of 100 classes, where each class has 5 sampling sequences

and there are 31 images in each sequence. The total number of images in the dataset

is 15,500 (100×5×31).

2.2. Image pre-processing

Each image in the dataset undergoes a pre-processing step consisting of localization

and normalization to eliminate any non-related information (e.g. sclera, pupil, etc.)

and to obtain an iris area with uniform size and shape. We have adopted the

pre-processing method presented in Ref. 43, to obtain a normalized image with

a resolution of 512×64. Fig. 3 shows an example of normalized iris images in a

particular sequence.

Performing iris feature extraction on the whole normalized image will lead to the

inclusion of pseudo features caused by irrelevant information that may still exist,

such as eyelids and eyelashes. In order to ensure that the image used for recognition

contains minimal irrelevant information, we can extract a smaller region of the

normalized image for feature extraction. However, if the chosen region is too small,

the features may not be sufficient for iris recognition. Based on the work in Ref. 44,

we chose the mid-top area, i.e. about 40% of the whole normalized image as it has

been shown to exclude features caused by irrelevant objects most of the time. The

region of normalized images chosen for feature extraction is shown in the rectangles

in Fig. 3. The resolution of the reduced normalized image is 260×32 pixels.



Fig. 3. Normalized iris image.

2.3. Feature extraction

Research in iris feature extraction for over a decade reveals that the Gabor filter

used by Daugman is unanimously considered as the most efficient band-pass fil-

ter for discriminating the iris texture when compared to other wavelet extraction

methods.8,39 A Gabor filter under rectangular coordinate is given by Eq. (1).

Gabor(x, y) = exp

{

−

[

(x − x0)2

α2+

(y − y0)2

β2

]}

exp {2πi[u0(x − x0) + v0(y − y0)]} .

(1)

In Eq. (1), (x0, y0) denote the centre position of the filter, (α, β) denote the

wavelet size in length and width, and the frequency and direction of the filter

depend on (u0, v0).

Binary quantification based on the complex phase vector is also widely adopted

for representing the iris features.37 As such, we have applied the 2D Gabor filter

on the normalized iris texture images, extracted the complex phase information,

and mapped them to binary codes based on their location on the complex plane as

shown in Fig. 4. For example, if the phase vector (consisting of real and imaginary

parts) lie in the top-right quadrant of the complex plane, it will be mapped to “11”.

As a result, a feature code matrix of 260×32×2 bits is generated for each iris image.

The real part and imaginary part, each a 260×32×1 bit matrix, is stored separately

to facilitate our analysis on the changes in the individual bit characteristics due to

the imaging noise.

In a non-invasive iris recognition system, different eye images that are acquired

from a single person are commonly subjected to rotating excursion that is perpen-

dicular to the main optical axis of camera. This problem needs to be rectified so

that we can analyze the corresponding bit characteristics in the various degree of

defocus of a particular iris image sequence. This is achieved by using the clear image

(16th image in the 1st sequence of a particular class) as the reference for shifting

the bits in other images from the same class to the left/right so that the Hamming

distance between them is minimized.



Fig. 4. Phase coding.

2.4. Proposed method 1: identifying stable bits for iris recognition

Traditional pattern matching process for iris recognition attempts to cluster features

from the same individual and perform classification of different individuals based

on the focused iris images. In this study, we aim to determine iris features that

are robust to imaging noise. These features are prevalent in all the iris images of

a particular sequence. As discussed above, each iris image can be converted into

two feature code matrices consisting of bit patterns. Features that are invariant

to imaging noise can therefore be identified by a constant bit value in the same

position of the feature code matrices of the entire image sequence. Hence, our goal

is to identify feature clusters from iris image sequences of an individual and perform

classification of sequences from different individuals. We first make the following

definitions:

Definition 1. Exactly Consistent Bit (ECB): A bit position in the feature code

matrix is denoted as ECB if it is always 1 or 0 in two or more feature code matrices.

Definition 2. Exactly Consistent Bit Rate (ECBR): The percentage of ECBs (in

both real and imaginary feature code matrices) with respect to the size of the real

and imaginary feature code matrices (i.e. 260×32×2).

As ECB represent bit positions in the feature code matrix that are invariant to

imaging noise, they form the ideal bits for pattern matching of defocused images.

ECBR gives an indication of the similarity in iris features in two or more feature

code matrices. Fig. 5 compares the average ECBR of 100 classes between 1) two im-

ages with same degree of defocus (as denoted by the x-axis) from different sequence

of iris images of the same class (asterisk line), and 2) two images from the same

class, one the defocus image and the other, the clear image (rhombic line). For the

rhombic line, it can be observed that the average ECBR of a defocused image (with



5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Image number

Ave

rage

EC

BR

Same degree of defocusDifferent degree of defocus

Fig. 5. Average ECBR of iris images from 100 classes.

respect to the 16th image which is the clear image) decreases with increasing degree

of defocus. This implies that the number of ECB in a defocused image is inverse

proportional to the amount of defocus in the iris image. Hence, if the ECB bits are

used for recognition, we can expect that the recognition performance will degrade

as the amount of defocus increases. On the other hand, the asterisk line shows that

the ECB between two defocus images of different sequences (but of the same class

and same degree of defocus), remains pretty consistent. The ECB corresponds to

the features that are invariant to imaging noise. However, these features are rare in

images that are obtained from camera systems with small DOF. Our experimental

results show that on average, the ECBR is only 9.86%, which is not sufficient for

iris recognition. Hence, there is a need to relax the constraints for identifying stable

bits in the feature codes for iris recognition.

Definition 3. Sensitivity: Each bit position in a feature code matrix can be de-

fined as an intrinsic feature if the value (1 or 0) is found to be dominant across the

entire sequence of images. It is defined as a sensitive feature otherwise. Sensitivity

is defined as the probability of a particular bit position in the feature code matrix

being a sensitive feature (in terms of %) in a sequence.

Definition 4. Stable Bit and Sensitive Bit: A bit position in the feature code

matrix is defined as a stable bit when its sensitivity is less than a predefined sen-

sitive threshold. On the other hand, if the sensitivity is higher than the predefined



Fig. 6. Iris feature codes from two different classes (1 and 9).

sensitive threshold, we denote the corresponding bit position as a sensitive bit.

Based on Def. 3 and Def. 4, Eq. (2) is used to determine whether the bit position

of the feature code matrix at coordinate (x, y) is a stable bit. Fk(i, j) is the feature

code matrix of the kth image in the sequence, n is the number of images in the

sequence, and t is the predefined sensitive threshold.

(x, y) ∈ {(i, j)|G(i, j) ≤ Ta or G(i, j) ≥ Tb}

G(i, j) =∑n

k=1Fk(i, j)

Ta = bn× tc

Tb = n− bn× tc+ 1

. (2)

Based on Eq. (2), Fig. 6 shows the iris feature code consisting of stable and

sensitive bits for images in two classes (i.e. 1-1 and 1-2 are images from different

sequences in the same class, and 9-1 and 9-2 are images from different sequences of

another class). The sensitive threshold is set at 30%. The black regions correspond

to the stable bits while white regions correspond to the sensitive bits. We can

observe from Fig. 6 that the distribution of stable bits from different sequences in

the same class is similar with each other. In addition, the distribution of stable

bits from different classes varies significantly. These observations demonstrate the

feasibility of using stable bits for pattern matching to discriminate defocus iris

images of different individuals.

3. Proposed Method 2: Identifying Stable Bits from a Single

Enrolled Image

In the previous section, we have described a simple method to identify stable bits

that correspond to features which are invariant to the optical noise from a sequence

of iris images. The method however, requires a sequence of iris images to be captured

with varying degree of defocus during the enrollment phase. This is not a practical



approach especially for mobile phone users. In this section, we propose a method to

identify stable bits from a single enrolled image. The proposed method is based on

our analysis of the displacement of the feature phase vector in the complex plane

for varying degree of image defocus. We will show in the following section that this

method can lead to good recognition performance.

The proposed method for identifying the stable bits is based on the likelihood

that the corresponding phase vectors will move across the axis in the complex

plane when the images are subjected to varying degree of defocus. Fig. 7 shows

an example of the displacement of a phase vector in a sequence of 31 iris images

with different degree of defocus. It can be observed that the phase vector lies in the

bottom left quadrant of the complex plane for most of the defocused images and

in other quadrants (such as the top left) for the remaining defocus images. Based

on the phase coding process described in Sec. 2.3, the displacement of the phase

vector from one quadrant to another will cause a change in the corresponding bit

value of the feature code. In other words, we can deduce the stability of a feature

bit in the iris code by analyzing the distribution of the corresponding phase vector

in different image defocus. For example in Fig. 7, we can observe that the real part

of the phase vector have a higher stability than the imaginary part. In particular,

the sensitivity of real part of the phase vector is 7%, and the imaginary part is

33%. A crude method for determining the stable bit in this example would be to

choose the real part of the feature code as the stable bit. However, this method still

requires the availability of a sequence of images with different degree of defocus.

Based on the same example, Fig. 8 shows how the real and imaginary part of

the phase vector evolved with the degree of defocus. It can be observed that the

displacement of the phase vector of defocus images from the clear image (the 16th

−2 −1.5 −1 −0.5 0 0.5 1 1.5 2−2

−1.5

−1

−0.5

0

0.5

1

1.5

2 Im

Re

Fig. 7. Displacement of a complex phase vector in a sequence of iris images.



5 10 15 20 25 30

−1.2

−1

−0.8

−0.6

−0.4

−0.2

0

Image number

Rea

l par

t of t

he p

hase

vec

tor

(a) Relationship between the displacement of the real part of the phase vector

with degree of defocus.

5 10 15 20 25 30

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

Image number

Imag

inar

y pa

rt o

f the

pha

se v

ecto

r

(b) Relationship between the displacement of the imaginary part of the phase vector

with degree of defocus.

Fig. 8. Gradual displacement of the phase vectors with respect to the degree of defocus.

image) increases gradually with the degree of defocus. In particular, the distance

of displacement from the clear image for the real/imaginary parts increases until it

crosses the imaginary/real axis of the complex plane. At this point, the correspond-

ing bit value in the iris code will toggle. Therefore, based on a clear image, we can



deduce that the real/imaginary part of a phase vector for a particular position in iris

phase feature template is less likely to cross the axes, if its absolute distance from

the imaginary/real axis is large enough. In other words, we can identify the stable

bits of a single enrolled image based on the absolute distance of the real/imaginary

part of the corresponding phase vector to the imaginary/real axis.

Based on this, we devised the following method for identifying stable bits from

an enrolled image. We first compute the absolute distance of the real/imaginary

parts of the phase vectors of the enrolled image from the imaginary/real axis of the

complex plane, and sort the absolute distances. Based on a predefined stable bit

extraction rate, we identify the stable bits that correspond to the real/imaginary

phase vectors with the higher absolute distances.

Eq. (3) describes the method to identify the stable bit at coordinate (x, y) that

correspond to the real/imaginary phase vector. P is the real/imaginary part of the

phase vector of size 260×32, F abs computes the absolute distance, F vecsort is the

function to sort the absolute distances and rearrange the matrix into vectors, N is

the number of bits, and T is the stable bit extraction rate.

(x, y) ∈ {(i, j)|P (i, j) ≤ A(Nstable)}

A = Fvecsort(Fabs(P ))

Nstable = dN × T e

. (3)

4. Experimental Results

In this section, we will present experimental results based on the 15,500 images

in the iris image dataset (as discussed in Sec. 2.1) to demonstrate the feasibility

of the proposed approach for iris recognition which relies on the stable bits that

are identified using the methods discussed in this paper. The experiments were

undertaken using Matlab on a 2.53-GHz PC with 3 GB RAM.We first evaluate the

recognition performance of the proposed method that is based on identifying the

stable bits from a sequence of images (Proposed Method 1 as discussed in Sec. 2.4)

and the proposed method that is based on identifying the stable bits from a single

enrolled image (Proposed Method 2 as discussed in Sec. 3). Next, we evaluate the

effects of changing the stable bit extraction rate on the recognition performance of

the proposed method. Finally, we will evaluate the recognition performance of the

proposed method when the images that are subjected to recognition are captured

within certain predefined range from the camera.

4.1. Recognition performance

In order to evaluate the recognition performance of the proposed methods, we have

implemented a conventional method based on Daugman’s approach (as described

in Section 2.2 & 2.3) for iris recognition of defocused image that relies on the entire

code representation (as opposed to using only stable bits in the proposed methods)



for purpose of comparison. In ProposedMethod 1, we have used a sensitive threshold

of 30% to obtain the stable bits from a sequence for each class (the 1st sequence

in each class). Hence, a total of 3100 images (i.e.100×31) were used to obtain the

stable bits. In Proposed Method 2, we have used a stable bit extracting rate of 70%

to obtain the stable bits from the clear image for each class (the 16th image in the

first sequence of each class). Hence, 100 images are used to obtain the stable bits

in Proposed Method 2. The experiments to evaluate the recognition performance

of the proposed methods are based on 12400 images (i.e.100×4×31) images in the

dataset that have not been used for identifying the stable bits.

Fig. 9 shows the inter-class and intra-class Hamming distance of the images in

the dataset. Note that for the conventional method, the entire feature code matrix

is used to calculate the Hamming distance, while only the stable bits are used in the

proposed methods for calculating the Hamming distance. It can be observed from

0 0.2 0.4 0.6 0.8 10

0.05

0.1

0.15

0.2

0.25

Hamming Distance

Per

cent

Intra−classInter−class

(a) Conventional method.

0 0.2 0.4 0.6 0.8 10

0.05

0.1

0.15

0.2

0.25

Hamming Distance

Per

cent


(b) Proposed method 1.

0 0.2 0.4 0.6 0.8 10

0.05

0.1

0.15

0.2

0.25

Hamming Distance

Per

cent


(c) Proposed method 2.

Fig. 9. Distribution of inter and intra-class Hamming distances for conventional and proposedmethods.



Fig. 9, the intersection between the intra-class and inter-class distance distributions

of the proposed methods is smaller than the conventional method. It can be seen

in Table 1, that although the average inter-class and intra-class distance has been

reduced in the proposed methods, the intra-class distance reduction is significantly

larger (e.g. intra-class reduction of 0.0523 as compared to inter-class reduction of

0.0046 in Proposed Method 2). This results in a sharp increase in the average dif-

ference between inter-class and intra-class distances in the proposed methods (e.g.

0.0915 as compared to 0.1392 for the conventional method and Proposed Method

2 respectively). As shown in Table 1, this leads to higher recognition performance

in the proposed methods. In particular, when the False Acceptance Rate (FAR)

is 0.5% for both methods, the False Rejection Rate (FRR) of the conventional

and Proposed Method 2 is 65.86% and 39.52% repectively. In addition, Proposed

Method 2 has a recognition performance gain of about 2 times over the conventional

method as the Equal Error Rate (ERR) of the conventional and proposed method is

23.55% and 12.71% repectively. These results demonstrate that the proposed meth-

ods, which relies on stable bits that are tolerant to the optical defocusing for iris

recognition, is capable of discriminating iris features of individuals from different

classes.

It can be observed from Table 1 that the average difference between inter-class

and intra-class distances in Proposed Method 1 is larger than Proposed Method 2.

The recognition performance of Proposed Method 1 is also evidently higher than

Proposed Method 2.

4.2. Execution time

We have also compared the registration and matching time of the proposed methods

with the conventional method. Registration time is the time taken to generate iris

code representation during the enrollment process. Matching time refers to the time

taken for recognition. Note that the time for image acquisition and pre-processing

is not considered as these are the same for all the methods considered. Table 1

Table 1. Comparison of recognition performance between conventional and proposed methods.

Evaluation Conventional Method Proposed Method 1 Proposed Method 2Standard Intra-class Inter-class Intra-class Inter-class Intra-class Inter-class

AverageHamming 0.2448 0.3363 0.1792 0.3263 0.1925 0.3317DistanceFRR(%) 65.86 29.78 39.52

(FAR=0.5% )EER(%) 23.55 9.015 12.71

RegistrationTime (ms) 282.65 10095.17 359.22RecognitionTime (ms) 13.90 11.23 10.06



shows that the registration time for the conventional method is the lowest. The

registration time for Proposed Method 1 is the highest as it requires a sequence

of images to obtain the stable bits. However, it is noteworthy that registration is

a one-time process and even then, the time taken by Proposed Method 1 is only

about 10 seconds.

The recognition time for the proposed methods are lower than the conventional

method as they do not require the entire iris code representation for matching. In

particular, Proposed Method 1 and Proposed Method 2 can achieve a reduction

in the recognition time of 19.2% and 27.6% when compared to the conventional

method.

From Table 1, it is evident that even though the recognition performance of

Proposed Method 1 is marginally better than Proposed Method 2 (less than 4%

improvement), Proposed Method 2 is a more feasible solution. In contrast to Pro-

posed Method 1, which requires a longer time to identify the stable bits, Proposed

Method 2 only requires a single enrolled image for identifying the stable bits, hence

resulting in significantly lower registration time. In addition, the recognition time

of Proposed Method 2 is lower than the recognition time of Proposed Method 1.

4.3. Effects of stable bit extraction rate on performance

The stable bit extraction rate T , which is used to determine whether a bit position

in the feature code matrix is a stable bit or not, is an important parameter as

it affects the recognition performance. Table 2 shows how the average recognition

performance (in terms of EER) changes when different stable bit extration rates

are used in Proposed Method 2. In particular, we have used stable bit extration

rates that are set to 55%, 60%, 65%, 70% and 75% to obtain the stable bits and

evaluated the iris recognition performance for all the images in the dataset.

It can be observed that the recognition performance increases when the sta-

ble bit extraction rate is increased from 55% to 65%. Thereafter, the recognition

performance decreases with further increase in the stable bit extraction rate. The

effects of the stable bit extraction rate on the recognition performance is expected,

as choosing a very small stable bit extraction rate will restrict the number of sta-

ble bits for recognition which could lead to low inter-class variability. On the other

hand, defining a very large value for the stable bit extraction rate will lead to higher

number of stable bits that are sensitive to the imaging noise and this will result in

large intra-class variability, which leads to lower recognition performance. Table 2

Table 2. Recognition performance with varying stable bit extraction rate.

Performance Stable Bit Extraction Rate (%)Standard 55 60 65 70 75

EER(%) 14.65 13.14 12.02 12.71 13.80Recognition Time (ms) 7.69 8.51 9.46 10.06 10.88



also shows that the recognition time increases with the stable bit extraction rate.

This is due to the increase in the number of stable bits that are used for matching

during recognition when the stable bit extraction rate increases.

In practice, we can also determine a suitable stable bit extraction rate to obtain

a good trade-off between the recognition performance and the computational com-

plexity of the iris recognition process. For example, in a multi-biometric system for

mobile phones, where the recognition performance of a single biometric system is

not so crucial, we may decide to choose a lower stable bit extraction rate (60%),

which is not with the best EER, to reduce the number of computations for iris

recognition (due to lesser amount of stable bits for pattern matching). This may

also lead to notable power savings for the mobile phone.

4.4. Effects of sampling range on recognition performance

In order to increase the recognition performance in a non-intrusive iris recognition

system, the user can be guided to position himself within a certain image capture

distance from the camera. It is noteworthy that such a system should not impose

strict requirements on the user to position himself such that the image will be

captured within the DOF of the camera. Instead, the system should provide a

distance range for image capture such that the entire process is comfortable to the

user and can be completed fast. In this section, we evaluate the effects of varying the

distance range of image capture on the recognition performance. We have chosen

images under different sampling range in the iris image dataset to create the test

sets. The sixteen test sets are shown in Table 3, where each set contains images

from varying distance range of image capture.

We evaluated the recognition performance of each test set using the conventional

and proposed methods. It can be observed from Fig. 10 that the EER of the three

methods decreases as the distance range of image capture increases. However, the

EER of the proposed methods is lower than the EER of the conventional method in

all cases. In addition, the EER of the conventional method increases more drastically

than the proposed method. These results further justifies the effectiveness of using

stable features to extend the DOF of iris recognition system.

Table 3. Select of testing database.

Test Set No. 1 2 3 4 5 6 7 8

Image No. in Sequence 16 15-17 14-18 13-19 12-20 11-21 10-22 9-23Range of Image Capture 0 4 8 12 16 20 24 28

(mm)

Test Set No. 9 10 11 12 13 14 15 16

Image No. in Sequence 8-24 7-25 6-26 5-27 4-28 3-29 2-30 1-31Range of Image Capture 32 36 40 44 48 52 56 60

(mm)



0 10 20 30 40 50 600

0.05

0.1

0.15

0.2

0.25

Acquisition Range ( AR ) = 0 : 4 : 60 ( mm )

Equ

al E

rror

Rat

e (

EE

R )

Proposed Method 2Proposed Method 1Conventional

Fig. 10. Relation diagram of AR and EER.

5. Conclusion

In this paper, we proposed a method to identify stable features in iris images that are

tolerant to imaging noise, which is introduced when the images are captured outside

the camera DOF. Our analysis reveals that the stable bits corresponding to the

stable features in the iris image can be identified based on the displacement of the

feature phase vector from the axes in the complex plane. This enabled us to devise

a method for identifying stable bits in a single enrolled image for iris recognition

of defocused images. Unlike existing methods, the proposed method lends itself

well towards robust, cost effective and power efficient iris recognition system for

mobile phones as it does not require special hardware and time consuming image

restoration algorithms. Experimental results demonstrate the superiority of the

proposed method over a conventional method that uses the entire feature code

representation for iris recognition. Future work includes exploring the benefits of

incorporating more accurate localization, feature extraction and matching in the

proposed method.

Acknowledgments

This work is partially supported by the National Natural Science Foundation of

China under Grant No. 60672078 and the China Scholarship Council (CSC).



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Bo Liu received herBachelor’s degree of En-gineering in ShenyangUniversity of Technol-ogy (SUT), Shenyang,China, in 2006. Cur-rently, she is a PhDcandidate in the schoolof Information Scienceand Engineering, SUT.

She also has been a Joint PhD student inthe Centre for High Performance EmbeddedSystems of Nanyang Technological Univer-sity (NTU), Singapore from Sep. 2010 to Sep.2011. Her research interests include biomet-ric identification, computer vision, image pro-cessing and pattern recognition, and also in-telligent embedded systems and its applica-tion.

Siew-Kei Lam re-ceived the BASc(Hons.) degree, MEngdegree, and PhD incomputer engineeringfrom Nanyang Tech-nological University(NTU), Singapore.Since 1994, he has beenwith NTU, where he is

currently a Research Fellow with the Cen-tre for High Performance Embedded Systemsand has worked on a number of challengingprojects that involved the porting of complexalgorithms in VLSI. He is also familiar withrapid prototyping and application specificintegrated-circuit design flow methodologies.His research interests include embedded sys-tem design algorithms and methodologies,algorithms-to-architectural translations, andhigh-speed arithmetic units. He is the mem-ber of the IEEE.



Thambipillai Srikan-

than joined NanyangTechnological Univer-sity (NTU), Singapore,in 1991. At present, heholds a full professorand joint appointmentsas a director of a 100strong Centre for HighPerformance Embedded

Systems (CHiPES). He founded CHiPES in1998 and elevated it to a university levelresearch centre in February 2000. He haspublished more than 250 technical papers.His research interests include design method-ologies for complex embedded systems, ar-chitectural translations of compute intensivealgorithms, computer arithmetic, and high-speed techniques for image processing anddynamic routing. He is the senior member ofthe IEEE.

Weiqi Yuan receivedhis PhD degree fromNortheast University(NEU), China in 1997.He is currently a pro-fessor of Shenyang Uni-versity of Technology(SUT) and the dean ofthe school of Informa-tion Science and Engi-

neering. He is the director of the ComputerVision Group of SUT and also the directorof SUT-Texas Instruments DSP Joint Lab-oratory. He has published more than 200technical papers. His main research inter-ests include biometric identification, com-puter vision, image processing and patternrecognition and image capture and process-ing system based on DSP. He is the standingdirector of China Instrument and ControlSociety.

iris recognition of defocused images for mobile phones

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