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ICSP2008 Proceedings

Multispectral Palmprint Recognition using Wavelet-based ImageFusion

Dong Han2, Zhenhua Guo

1, David Zhang

1

1 Biometric Research Centre, Department of Computing, the Hong Kong Polytechnic University, HongKong

2Department of Automation, Tsinghua University, Beijing,

China

E-MAIL: don g .h an .1 9 83 @ g m a il.c om, cszguo@comp.polyu.edu.hk,

csdzhang@comp.polyu.edu.hk,

Abstract: Palmprint is widely used in personal identification foran accurate and robust recognition. To improve the existing palmprint systems, the proposed system, which is the firston-line multispectral palmprint recognition system everdesigned before, uses multispectral capture device to senseimages under different illumination, including Red, Green, Blueand Infrared. We adopt Competitive Coding Scheme as

matching algorithm, which performs well in on-line palmprint recognition. Wavelet-based image fusion methodis used as data-level fusion strategy in our scheme. Fusedverifications show better effort on motion blurred sourceimages than single channel. Experimental results of fusionimages are also useful references for future work onmultispectral palmprint recognition.Key words: palmprint recognition, multispectral biometrics,

wavelet transform, image fusion, motion blurring

1. IntroductionThe increasing demand for personal identification is calling for

more convenient and secure systems than traditional methods,

i.e. passwords, ID cards, which could be forgotten or lostoccasionally. Biometrics, identification/verification of a person

by the physiological or behavioral characteristic, is playingan important role in modern personal identification

systems[4].

More and more biometric features are proposed and used

in commercial systems, such as fingerprint, palmprint,

facial feature, voice, iris, etc. However, there is not a perfect

biometric method that can suffice all the situations. For

example,

fingerprint is the most widely used biometric feature, but some

workers have so bad fingerprints that cant be recognized well.

Of all the biometric authentication methods, palmprint

recognition is one of the most user-friendly and reliable

methods. Palmprint is concerned with the inner surface of the

hand. It is

unique between people, even palms of one single persons twohands or twins palmprints[9]. Compared to fingerprint, the

most widely used biometric feature in the past 25 years[1],

palmprint has several advantages: more acceptable when

captured; low-resolution imaging can be employed; workers or

elderly people may not provide clear fingerprint but could offer

clear palmprint; palmprint image could provide even more

information than fingerprint[10]. An efficient algorithm

using

Competitive Coding Scheme[2], which we use in

our experiments, can be used for fast palmprint recognition in

online system.While palmprint-based authentication approaches have shown

excellent results, a higher performance is still needed in somehigh security situation. Very few researchers have

considered multispectral algorithm to improve theeffect of the palmprint-based personal recognition.Multispectral imaging is widely used in remote sensing,medical imaging and machine vision. Several images can be provided at one same scene but with different information.Some papers were published in the area of multispectral biometrics which can be important references. RK.Rowe[12] did some work on biometrics using multispectral

skin texture. C Boyce[8] published their research onmultispectral iris recognition. H.Chang and A.Koschan [11]

978-1-4244-2179-4/08/$25.002008 IEEE

mailto:1983@gmail.commailto:1983@gmail.commailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:cszguo@comp.polyu.edu.hkmailto:csdzhang@comp.polyu.edu.hkmailto:1983@gmail.com

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used wavelet-based multispectral image fusion on facerecognition and got a good performance. There is few paper published on multispectral palmprint recognition undervisible spectrum. [5] compared some algorithms ofmultispectral palmprint image fusion, but did not proposea recognition system. [6] and [7] s work are relatedto multispectral palmprints, but the images using in thesystem were at a resolution of higher than 500DPI, whichmay not able to meet fast computation requirement.

The objective of this paper is to compare performancesof palmprint recognition under different spectral

wavelengths, including Red, Green, Blue and Infrared

spectrum. We also

present a multispectral palmprint recognition system using fused

images which combines palmprint images captured at different

channels. Wavelet transform, which is widely used on

image fusion, is adopted in our strategy. We concerned the

situation of

hand movement during images capturing, Experimental results

under different illumination were compared to each other,

in order to find out the best spectrum at which the

palmprint recognition system performs, and to find a better

recognition

method on blurred hand images.The rest of the paper is organized as follows. We give details

of the system framework, including the multispectral palmprint

capture hardware and algorithm of palmprint recognition in

Section 2. The wavelet-based image fusion strategy is described

in Section 3. Motion Blurring is discussed in Section 4,

and Experimental results are shown in Section 5. Finally,

the conclusions are given in Section 6.

2. System Framework

2.1. Multispectral Palmprint Capturing

As we know, Green, Red and Blue could composite different

light in visible spectrum. Moreover, most of color images are

recorded or represented by these three colors. Infrared light

could offer pictures with more penetrability. We built a

multispectral system using these 4 illuminations. The peaks

of red, green, blue and infrared light are 660nm, 525nm, 470

nm and 880nm respectively. Our illuminator is a LED array,

which arranged in a circle to provide a uniform illumination.

The LED array can switch to different light in about 100ms.

The palmprint capture device also includes CCD camera, lens,

and A/D converter. When a person put his palm on the

case which forms a semi-closed environment, the light-

sensitive device can start the sampling step. Automatic sampling

will run at 4 spectrums. Four different palm images, with the

resolution lower than 100 DPI to suit the online application,

will be transmitted to a computer in 500ms.

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2

'

2 2

Figure.1. Theprototype device.

2.2. Palmprint recognition systemA palmprint can be represented by some line features or texture

features from a low-resolution image. Competitive

coding scheme, which was declared a fast and effective

algorithm.for online palmprint matching[2], is adopted in our system. To

get the match result of two palms from the texture feature, the

following steps are necessary after passing the

palmprint

pictures captured from the device to a computer[1]:

Step 1: Set up a coordinate system to align different palmprint images and extract the central part called Region

of Interest (ROI) for matching.

Step 2: Feature extraction from ROI pictures and coding a

feature vector.

Step 3: Matching two feature vectors to get a measurement

of the similarity between two palmprints.

2.2.1. Preprocessing. A reliable ROI extraction strategy

was proposed by [1]. The gaps between the fingers are

used as

represents the direction of the filters which has 6 choices.

2.2.3. Matching Algorithm. The competitive code is one of the

6 options: 0,1,2,3,4 and 5. They were coded by a 4-bit vector inwhich 3 bits were used to represent the competitive code and theother bit for mask. The similarity of two palmprints can be measured from their competitive code vector using

angular distance measurement. The detail information can bereferred in [2].

3. Fusion strategyFusion techniques integrate different data sources or multiple

classifiers to improve the performance of the system. Fusion

Strategy could be operated on 3 levels: data-level, feature-level

and score-level. Our work is focused on data-level, which based

on image fusion using wavelet transform.

The image fusion method tries to solve the problem

of combining information from several images taken from

the same object to get a new fused image. The wavelet-

based approach is widely used in image fusion. First, the

Discrete

Wavelet Transform (DWT) can decompose one single image in

different kinds of coefficients preserving the image information.

Second, the coefficients abstracted from different images can be

combined to obtain new coefficients, so that the information in

different images is appropriately collected. Last, the fused

image can be achieved from Inverse Discrete Wavelet

Transform (IDWT), so the merged coefficients can be presented

as the final fused image which also preserves the information. A

general wavelet-based image fusion can be described by:

reference points to locate a line segment as Y-axis.The

perpendicular bisector of the line segment can be determined to

IFU

= IDWT((DWT(I1),DWT(I

2),DWT(I

3

),...))

(2),

be the X-axis. A subimage of a fixed sizebased on

the coordinate system is extracted from the central area of

the

Where is the fusion strategy, DWT and IDWT is a

pair of invert functions representing the wavelet-transform,

palmprint image. Then the feature extraction step canbe

operated on the 128h128 ROI image.

2.2.2. Feature Extraction. The following form of

Gabor function was used in the competitive code scheme[2]:

I1 ,I2 ,I3fused image.

are the original images, and IFU

is the final

( 4x'2

+y'2

)

(x, y,,)

=2 e

8 eix '

e 2 (1)

In the formula, x'

=

(x

x

0 ) cos

+

(y

y

0 ) sin ,

y = (x x0

)sin + (y y0 )

cos;(x

0,y

0) is the center of the

function; is the radial frequency per unit length; is the

orientation of the Gabor functions. and are both used

2+1

(a) (b)

in radians. is denoted as = 2 ln 2 2

,

1

Figure.2. DWT of palmprint ROI: (a) palmprint ROI, (b) level 2decomposition ofDWT

where is the half-amplitude bandwidth of the frequency

response. while varies in six differentFigure.2. shows an example of DWT decompositions

at level-2 using Haar wavelet[13]. An original image

is

directions, 0,6

, ,3 2

, 23

and5 , in the coding scheme.6

transformed into a set of approximation and details components

using DWT at each level. The image can be

completely retrieved by the components using IDWT level by

level, for noSo the complex function can be six different values at each

pixel .

According to the competitive coding scheme, the

feature extracted from a palmprint ROI picture at each pixel was

information is lost during the decomposition. The waveletcoefficients of the fused image can be obtained by computing a

weighted average of the sources, or select a certain channel

instead. In our strategy called Min-Max strategy, we

choose the minimal approximation components in several

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originaldefined as: min j (I(x, y) R (x, y,w,j )) , channels as the fused approximation components, and thewhere I(x,

y)is the palmprint ROI picture, maximal details components as the fused details components.

The sketch map of the fusion scheme is shown in Figure.3,

R (x, y,w,j)

is the real part of the Gabor function , and an example in Figure.4.

j={1,...,6}

is the operator of convolution and

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12

4 4

Source Multiscale Fused Fused Image Whereg(x,

y)is the blurred image, f(x,

y)is the

Images Multiscale source image. Thre Fourier Transform of (3) is

G(u,v) = H(u,v)F(u,v) , where F(u,v)

is the

Fourier Transform of

derived from:f(x,y) ,and

H(u,v)

can be

DWT H(u,v)=

T

(ua + vb)sin[(ua + vb)]e

j(ua+

vb )

(4)

Where a, b can be derived from

x (t) = at

, y0T

0 (t) = bt

T

. The model is referred in [14].

The Motion Blurring Model is confirmed when a set of

Figure.3. Block diagram of Wavelet based image fusion parameters of T, a,

b

is fixed.

Figure.5. shows the real blurred image in our database andthe simulated image using motion blurred method. To simulatethe poor source images capturing when hand moving, pictures inall 4 channels were chosen randomly to be blurred in ourexperiments. Blurred images were put into the step offeature extraction, then matching. The experimental results onblurred images are shown in chapter 5.

(a) Original ROI images: infrared, red, green andblue (arranged

left to right, up to down)

(b) FusedImage

Figure.4. Image fusion of Infrared, Red, Green and Blue

channels. 3-level DWT using haarwavelets, fusion with

Min-Max strategy

4. Motion BlurringThe palm pictures can not be captured with an assuring

high

quality in the practical application. Movement during capturing

or inaccurate focusing will both form blurred images,

which

(a).Original ROI (b) BlurredROI

Fugure.5. ROI Image Blurring

5. Experimental results

5.1. Palmprint Database

Our test database consists of 500 different palms, and every

palm was sampled 12 times in two sessions with a time intervalover 5-15 days. So there are 12500=6000groups of palmprintimages in our database. Every group contains 4 palmprintimages sensed at the same scene under 4 different kinds ofillumination, including Red, Green Blue and Infrared.The database is the biggest multispectral palmprint databaseas we know.

5.2 Image BlurringWe randomly chose 1000 images from each channel of Red,

Green Blue and Infrared to blur by the method mentioned

in chapter 4. The parameter T was denoted 1, and the

parameters a

would affect the accuracy of the recognition. The performance

of the palmprint recognition at a single channel would beand b was denoted as:a= a0 10 ,b= b0 10 , where

reduced if the capture step is not monitored in a real system.Wavelet-based image fusion can fuse the information

of more than one channels, poor image in single channel would

be

a0,b

0are integers among 1 to 10 randomly.

repared in fused image. Our experimental results show that

image-level fusion by wavelet transform has a

good performance on recovering the palmprint source images.

Since the palmprint images in our database were captured

5.3 Results and discussion

We tried wavelet-based image fusion of 2, 3 or 4 channels. After

fusion in every experiment, each group of palmprintwas matched to all the others. Genuine match

happens

by a strict monitoring, which is quite different a situation from C2

500 =33,000

times, and there are

the practical application, we simulated the situation that 2 2movement occurs when palm image captured. Assuming that a C6000 C12 * 500 = 17,964,000

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impostor matches.

shutter opening in a short time T, the image that captured by

the sensor should be:

T

The Equal Error Rate (EER) is computed and listed in Table.1.

Matching experiments were also operated on original palmprint

images to find a spectrum with a better performance.

g(x,y)= f[x x0 (t),y y0(t)]dt

0

(3) The Table.1. also shows the result of wavelet-based image

fusion on the original palmprint database. Our fusion

strategy doesnt provide a better performance than the single

channel,

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the possbile reasons are: 1.Competitvie code scheme,the matching algorithm weve chosen, is more accurate butsensitive at the same time. Pixel in the fused image can be quitedifferent from the neighbor pixels if palmprint showsdifferent performances in 4 different illuminations. 2.Theaccuracy of the palmprint recognition is already at a high-level.The reasons of false identifications are always in relation to

the posture of the palm when capturing, the ROI extraction before matching, etc, which can hardly be improved in animage fusion algorithm.

The experimental results show that wavelet-based image

fusion has a better performance than single channel on

poor

source images. It is more robust and accuracy than single

channel in real system that cant make sure high-quality images

were captured. That proves image fusion can recover the source

images to some degree in order to make a robust recognition.We can also see recognition on red channel is moreaccuracy

than the other 3 channels. Thats because shorter illuminationcould show a more lucid texture. In the same time, illuminationof longer wavelength could get vein information of the palm because of the penetrability, Images captured under

infrared illumination shows palm vein, but the palmprintdetails are too faint to recognize.

Under red illumination, both texture details of the

plamprint surface and the information ofpalm vein could

be

extracted from the images captured. The performance of 4 kinds

of illumination could be referred in figure.4.

Table.1. EER of the system using wavelet based image

fusion, both on original images and blurred source images

Source Images EER/%

Red 0.0248

Green 0.0529

Blue 0.0515Infrared 0.0396

RGBI Fusion 0.0696

Blurred Red 0.0822

Blurred Green 0.1307

Blurred Blue 0.1363

Blurred Infrared 0.0849

Blurred RGBI Fusion 0.0786

6. Conclusion and future workThis paper presents the multispectral palmprint recognition

system using wavelet-based image fusion. Multispectral

palmprint capture device was designed to offer illuminations ofRed, Green, Blue and Infrared. So far as we know, it wasthefirst attempt on image fusion using Red, Green, Blue and

Infrared channels in a palmprint recognition system. The

verification results on different illumination are irradiative

forchoosing the best spectrum for palmprint recognition. Inour

experiments, illumination of Red suits the matching algorithm

better than the others.

The wavelet-based image fusion algorithm on palmprint

images was attempted for primary experiments.

Experimental results showed that the algorithm is effective on

blurred images which are a good simulation of unmonitored

image capturing. Some results obtained were useful referencesfor future research

on multispectral palmprint recognition.

Our future work will focus on more effective fusion strategy

and special feature extraction algorithm of fused images, to

achieve a more accurate and robust multispectral palmprint

recognition system.

7. Acknowledgements

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The work is partially supported by the CERG fund fromthe HKSAR Government, the central fund from HongKong Polytechnic University, and the NSFC/863 funds underContract No. 60620160097 and 2006AA01Z193 in China.

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[3] G. Pajares and J. Manuel de la Cruz, "A wavelet-

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[5] Y. Hao, Z. Sun, and T. Tan, "Comparative Studies

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[6] L.L. Sulem et al., "Final Report on the jointlyexcuted research carried out on signature, handand other modalities", Contract Number: IST-2002-507634, Deliverable Number: D7.7.7, ProjectAcronym: BioSecure Project Title: Biometrics for SecureAuthentication, Start Date of Project: 01 Jun, 2004.

[7] R.K. Rowe et al., "A Multispectral Whole-Hand BiometricAuthentication System", Biometrics Symposium,2007, pp.

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[8] C. Boyce et al., "Multispectral Iris Analysis: A Preliminary

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