ICSP2008 Proceedings
Multispectral Palmprint Recognition using Wavelet-based Image Fusion Dong Han2, Zhenhua Guo1, David Zhang1 1
Biometric Research Centre, Department of Computing, the Hong Kong Polytechnic University, Hong Kong 2 Department of Automation, Tsinghua University, Beijing, China E-MAIL:
[email protected],
[email protected],
[email protected],
Abstract: Palmprint is widely used in personal identification for an accurate and robust recognition. To improve the existing palmprint systems, the proposed system, which is the first on-line multispectral palmprint recognition system ever designed before, uses multispectral capture device to sense images under different illumination, including Red, Green, Blue and Infrared. We adopt Competitive Coding Scheme as matching algorithm, which performs well in on-line palmprint recognition. Wavelet-based image fusion method is used as data-level fusion strategy in our scheme. Fused verifications show better effort on motion blurred source images than single channel. Experimental results of fusion images are also useful references for future work on multispectral palmprint recognition. Key words: palmprint recognition, multispectral biometrics, wavelet transform, image fusion, motion blurring 1. Introduction The 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 lost occasionally. Biometrics, identification/verification of a person by the physiological or behavioral characteristic, is playing an 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 can’t 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 person’s two hands 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 some high security situation. Very few researchers have considered multispectral algorithm to improve the effect 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 on multispectral iris recognition. H.Chang and A.Koschan [11]
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used wavelet-based multispectral image fusion on face recognition and got a good performance. There is few paper published on multispectral palmprint recognition under visible spectrum. [5] compared some algorithms of multispectral palmprint image fusion, but did not propose a recognition system. [6] and [7] ‘s work are related to multispectral palmprints, but the images using in the system were at a resolution of higher than 500DPI, which may not able to meet fast computation requirement. The objective of this paper is to compare performances of 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.
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 in which 3 bits were used to represent the competitive code and the other bit for mask. The similarity of two palmprints can be measured from their competitive code vector using angular distance measurement. The detail information can be referred in [2]. Figure.1. The prototype device. 2.2. Palmprint recognition system A 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 reference points to locate a line segment as Y-axis. The perpendicular bisector of the line segment can be determined to be the X-axis. A subimage of a fixed size based on the coordinate system is extracted from the central area of the palmprint image. Then the feature extraction step can be operated on the 128h128 ROI image.
3. Fusion strategy Fusion 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: I FU = IDWT (Φ ( DWT ( I1 ), DWT ( I 2 ), DWT ( I 3 ),...)) (2),
Where Φ is the fusion strategy, DWT and IDWT is a pair of invert functions representing the wavelet-transform, I 1 , I 2 , I 3 are the original images, and I FU is the final fused image.
2.2.2. Feature Extraction. The following form of Gabor function was used in the competitive code scheme[2]: ω2
ω ψ ( x, y , ω , θ ) = e 8κ 2π κ
2
2
2
( 4 x' + y ' )
κ § i ωx ' − ¨e − e 2 ¨ ©
2
· ¸ ¸ ¹ (1)
x ' = ( x − x 0 ) cos θ + ( y − y 0 ) sin θ , y ' = ( x − x0 ) sin θ + ( y − y0 ) cos θ ; ( x0 , y0 ) is the center of the
In
function;
the
formula,
σ
is the radial frequency per unit length;
ω
orientation of the Gabor functions. in radians.
κ
and
θ
θ
is the
are both used
§ 2δ + 1 · ¸¸ , κ = 2 ln 2 ¨¨ δ © 2 − 1¹
is denoted as
where δ is the half-amplitude bandwidth of the frequency response.
while
directions, 0,
θ
varies
,
and
π π π 2π ,
,
6 3 2
3
5π 6
in
six
different
, in the coding scheme.
So 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 defined
as:
min j ( I ( x, y ) ∗ψ R ( x, y , w, θ j ))
I ( x, y ) ψ R ( x, y, w,θ j ) where
is
the
palmprint
ROI
,
picture,
is the real part of the Gabor function ,
j = {1,...,6} ˈĀ ∗ ā is
(a) (b) Figure.2. DWT of palmprint ROI: (a) palmprint ROI, (b) level 2 decomposition of DWT Figure.2. shows an example of DWT decompositions at level-2 using Haar wavelet[13]. An original image is 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 no information is lost during the decomposition. The wavelet coefficients 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 original channels as the fused approximation components, and the maximal details components as the fused details components. The sketch map of the fusion scheme is shown in Figure.3, and an example in Figure.4.
the operator of convolution and
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Source Images
Multiscale
Fused Multiscale
Fused Image
Where g ( x, y ) is the blurred image, f ( x, y ) is the source image. Thre Fourier Transform of (3) is G (u , v) = H (u , v) F (u , v) , where F (u , v) is the
f ( x, y )
Fourier Transform of derived from:
H (u , v) =
DWT
Where
, and
H (u , v)
can be
T sin[π (ua + vb)]e − jπ ( ua + vb ) π (ua + vb) a,
b
can
be
derived
(4) from
t t . The model is referred in [14]. x 0 (t ) = a , y 0 (t ) = b T T Figure.3. Block diagram of Wavelet based image fusion
The Motion Blurring Model is confirmed when a set of parameters of T , a , b is fixed. Figure.5. shows the real blurred image in our database and the simulated image using motion blurred method. To simulate the poor source images capturing when hand moving, pictures in all 4 channels were chosen randomly to be blurred in our experiments. Blurred images were put into the step of feature extraction, then matching. The experimental results on blurred images are shown in chapter 5.
(a) Original ROI images: infrared, red, green and blue (arranged left to right, up to down)
(a).Original ROI (b) Blurred ROI Fugure.5. ROI Image Blurring
(b) Fused Image Figure.4. Image fusion of Infrared, Red, Green and Blue channels. 3-level DWT using haar wavelets, fusion with Min-Max strategy
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 interval over 5-15 days. So there are 12 × 500=6000 groups of palmprint images in our database. Every group contains 4 palmprint images sensed at the same scene under 4 different kinds of illumination, including Red, Green Blue and Infrared. The database is the biggest multispectral palmprint database as we know.
4. Motion Blurring The 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 would affect the accuracy of the recognition. The performance of the palmprint recognition at a single channel would be 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 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 by a strict monitoring, which is quite different a situation from the practical application, we simulated the situation that movement occurs when palm image captured. Assuming that a shutter opening in a short time T, the image that captured by the sensor should be: T
g ( x, y ) = ³ f [ x − x 0 (t ), y − y 0 (t )]dt 0
2076
(3)
5.2 Image Blurring We 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 and b was denoted as: a
a 0 ,b0
= a0 × 10 −4 , b = b0 × 10 −4 , where
are integers among 1 to 10 randomly.
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 palmprint was matched to all the others. Genuine match happens times, C122 × 500 = 33,000 2 2 C 6000 − C12 * 500 = 17 ,964 ,000
and
there
impostor
are
matches.
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. The Table.1. also shows the result of wavelet-based image fusion on the original palmprint database. Our fusion strategy doesn’t provide a better performance than the single channel,
the possbile reasons are: 1.Competitvie code scheme, the matching algorithm we’ve chosen, is more accurate but sensitive at the same time. Pixel in the fused image can be quite different from the neighbor pixels if palmprint shows different performances in 4 different illuminations. 2.The accuracy 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 an image 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 can’t 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 more accuracy than the other 3 channels. That’s because shorter illumination could show a more lucid texture. In the same time, illumination of longer wavelength could get vein information of the palm because of the penetrability, Images captured under infrared illumination shows palm vein, but the palmprint details are too faint to recognize. Under red illumination, both texture details of the plamprint surface and the information of palm 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 Red Green Blue Infrared RGBI Fusion Blurred Red Blurred Green Blurred Blue Blurred Infrared Blurred RGBI Fusion
EER/% 0.0248 0.0529 0.0515 0.0396 0.0696 0.0822 0.1307 0.1363 0.0849 0.0786
6. Conclusion and future work This paper presents the multispectral palmprint recognition system using wavelet-based image fusion. Multispectral palmprint capture device was designed to offer illuminations of Red, Green, Blue and Infrared. So far as we know, it was the first attempt on image fusion using Red, Green, Blue and Infrared channels in a palmprint recognition system. The verification results on different illumination are irradiative for choosing the best spectrum for palmprint recognition. In our 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 references for 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.
The work is partially supported by the CERG fund from the HKSAR Government, the central fund from Hong Kong Polytechnic University, and the NSFC/863 funds under Contract No. 60620160097 and 2006AA01Z193 in China. 8. [1]
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Acknowledgements
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