Finger-vein Image Separation Algorithms and Realization with

MATLAB Xiaoyan Gao, Junshan Ma, Jiajie Wu

School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

ABSTRACT

According to the characteristics of the finger-vein image, we adopted a series of methods to enhance the contrast of the

image in order to separate the finger-vein areas from the background areas，and made prepare for the subsequent research

such as feature extraction and recognition processing . The method consists of three steps: denoising, contrast enhancement and image binarization. In denoising, considering the relationship between gray levels in the adjacent areas of the finger-vein image, we adopted the Gradient Inverse Weighted Smoothing method. In contrast enhancement, we improved the conventional High Frequency Stress Filtering method and adopted a method which combined the traditional High Frequency Stress Filtering algorithm together with the Histogram Equalization. With this method, the contrast of the finger-vein area and the background area has been enhanced significantly. During the binarization process, after taking the differences of the gray levels between the different areas of the finger-vein image into consideration, we proposed a method which combined the binarization by dividing the image into several segments and the Morphological Image Processing means. Our experiment results show that after a series of processing mentioned above by using MATLAB, the finger-vein areas can be separated from the background areas obviously. We can get a vivid figure of the finger-vein which provided some references for the following research such as finger-vein image feature extraction, matching and identification. Key words: finger-vein, Gradient Inverse Weighted, image enhancement, high frequency stress filtering, equalization, Sub-regional average

1. INTRODUCTION

Personal identification technology has been applied in many domains including PC login, E-commerce such as ATM, area-access control and so forth. The traditional means such as using keys, cards, password or PIN code to achieve your goals will exit some dangerous , for example, you may loss the keys, forget the passwords, or your cards will be stolen[1]. In 2000, M.Kono and his colleges first developed the finger-vein identification system which based on the biometrics with the help of Hitachi Company [2, 3] and then they applied it to identify individuals. Compare with the other technologies, finger-vein identification method has many distinct features and advantages. Firstly, as veins exist beneath a human being’s skin, there has so little impact on it when the surface of the finger is injured or in some other bad conditions that we will have less identifying barriers. Secondly, this method bases on the live body. It is, therefore,

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impossible for any other people to steal or copy the biometric patterns and so it’s safer. Thirdly, because that there is no need to contact the device when you use this system, it’s more sanitary. Finally, the characteristics of the finger-vein won’t be changed with time goes by, and what’s more, the different individual has the different finger-vein characteristics, so it’s steady and unique. In this system, a finger is placed between an infrared light source and the CCD camera, and then let the infrared light transmit from the backside of the finger, as hemoglobin in the blood of the finger-vein absorbs the infrared light; the pattern of veins in the palm side of the finger is captured as a pattern of shadows. The captured images contain not only vein patterns but also irregular shading and noise. The shading is produced by the varying thickness of finger bones and muscles. The irregular shading will do badly to the next identification processing. So it’s an important step to process the captured image by denoising and enhancement and to separate the finger-vein areas from the background areas. In this article, we propose some new methods to achieve this goal.

2. GRADIENT INVERSE WEIGHTED SMOOTHING METHOD In the image captured by the CCD camera, there exists noise and some irregular shading which caused by the different thickness of the muscles and the bones, or maybe the illumination of the light resource itself is uneven. Therefore, the vein region is not sharply visible in the image. This phenomenon will affect the following process such as feature extraction and matching, so the image denoising is a crucial step in the whole processing. However, not only the noise but also the significant changes of the grey information of original image will be smoothed into so-called blur image if we just use a single way of median filtering. The algorithm we adopted in this section will reduce damages to the edge and the lines in the original image. We call this algorithm The Gradient Inverse weighted smoothing method. This method based on the variable weighted average method and the size of the window is 33× . We use this mean to restrain the noise. The weight coefficient matrix W [4, 5] of each local area will be determined by the following way:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

++++−+−

−+−−−=

)1,1()1,()1,1(),1(),(),1(

)1,1()1,()1,1(

jiwjiwjiwjiwjiwjiw

jiwjiwjiwW (1)

21),( =jiw (2)

∑∑×

=

k llks

lkslkw

),(

),(21

),( (3)

1,0,1, −=lk

),(),(1),(

jifljkiflks

−++= (4)

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In these equations , (.)f is the finger-vein image showed in Fig.1 and with the size of mn× , ),( lks represents

the reciprocal of the difference between the pixel in the middle of the matrix and the pixels of the neighbor area

and ),( lks =2 if ),(),( jifljkif =++ .The value of the weight coefficient matrix proportions to the reciprocal of

the difference between the pixel in the middle of the local area and the pixels in the neighbor region, so the difference is smaller the value is bigger, and vice versa. By this way, we can avoid the distinct changes of the gray level and so we can eliminate the noise but do little damages to the edge of the finger-vein image. After that, we process the image by histogram equalization and then we can get a clearer image. Fig.2 is the image after the above processing. As the following figures show, compared with Fig.1, Fig.2 is much better.

Fig. 1 Original image Fig. 2 Image after denoising

3. CONTRAST ENHANCEMENT——BY COMBINING THE HIGH-FREQUENCY EMPHASIS FILTERING ALGORITHM WITH THE HISTOGRAM EQUALIZATION ALGORITHM

After the processing in section 2, although we reduced the noise of the original image, the image is not clearly enough to identify. The conventional way solving this problem is to use the High Frequency Stress Filtering algorithm, but we can’t get the ideal image by using this method. Fig.4 shows the best evidence. So we combined the High Frequency Stress Filtering algorithm with the Histogram Equalization algorithm to enhance the contrast. The High-pass filter multiplies by an invariable coefficient greater than 1, and then pluses a constant, all above make up the transfer function of the High -frequency Emphasis filter. The coefficient gives prominence to the high-frequency part of the image while at the same time also increases the low-frequency part of it, but as long as the constant is smaller than the coefficient, the impact on enhancement to the high-frequency is much greater than the low-frequency. From above, we know that the filter transfer function Hgp (u, v) [6] can be expressed as follows:

),(),( vuHbavuH hpgp ×+= (5)

In equation (5), a is the value of excursion, and b is the coefficient while ),( vuHhp is the transfer function of the

high-pass filter. The merit of the High-frequency Emphasis filter is that the grayscale tone which caused by the low frequency is maintained. As the finger-vein image is characterized by gray in the narrow range of gray-scale, it’s the ideal choice for Histogram Equalization. So in this section, we combined the two, and the experiment results proved that it’s a reasonable way to do so. As the image used in this article, the infrared light did not pass through the fingertip, so the vein lines was not visible, and this problem which can be resolved by the hardware circuit, was not in the debate of this article. Therefore, we used the part of the image which the infrared light passed through. Fig.3 is the image after smoothing, and Fig.4 shows the image by using the High-frequency Emphasis filter algorithm while Fig.5 is the image

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after using the combining method and all these images produced by MATLAB programs. Compared with Fig.4 and Fig.5, we can see that the result which used the way above is much better than the result by using the conventional method, while compared with Fig.3 and Fig.5; we can see that the contrast of the image has been enhanced a lot.

Fig. 3 Image after denoising Fig. 4 Image after high frequency stress filtering Fig. 5 Image after the combined method

4. SEPARATE THE IMAGE ——BY COMBINING BINARIZATION AND MORPHOLOGICAL IMAGE PROCESSING METHOD

As the values of gray in the image are differences between the various parts of the image, so if we use a single threshold value for binarization processing, there will exist distortion to large degree. In this section, we proposed a method, that was, firstly, dividing the image after the above processing into several areas, and the size of each area was 5151× , and then computing the average of the gray value in each area. Finally, comparing the value of each pixel in the area with the average, if the value of some pixel was smaller than the average, then the value of the pixel was set to 0 which represented the finger-vein area, if the value of some pixel was greater or equal to the average, and then the value of the pixel was set to 1 which represented the background area. After that, we got a binary image, making the finger-vein area and the background area separated clearly. When we realized this algorithm by MATLAB program, the experiment results show as follows. Fig.6 is the same as Fig.5, and Fig.7 is the image after binarization processing. We can see that there still exists some discontinuous noise in Fig.7, so we had adopted a morphological image processing method which called as closing operation [6]. Firstly, the image was dilated in order not to make the image distortion too much, and then the image was eroded to eliminate the noise. Dilating is an operation which lengthens or coarsens the binarization image. A set called structural element controls this operation. Usually, in computing, we can use a matrix composed by 0 and 1to express the structural elements. In this process, the origin of the structural elements will be moved across the whole image one pixel by one pixel, and we should check which pixel in the image overlaps with the origin of the structural elements, and then cover the surrounding pixels of the areas with the structural elements. In mathematics, we define the processing of dilating as a set operation. When A dilated by B, we sign it as A B⊕ :

{ }Z

Z|( B) AA B∧

⊕ = ≠ ΦI （6）

In equation (6), Φ is a void set，A is the image we decided to process，B is the set of structural elements. In conclusion,

the result of A dilated by B is a set which composed by the origins of all the structure elements，in the set, there at least

exists some part in B which after being mapped and moved can overlap with the image A. After that, we processed the image by eroding in order to eliminate the noise. It’s an operation which constringes the

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object of the binarization image. A set called structural element controls the way and the degree of this operation. In the process of eroding, the origin of the structural elements should be moved across the whole areas of the image one pixel by one pixel, and we should check out which areas in the image can match with the structural elements. In these areas, the value of the pixel which overlapped with the origin will be set as the value of the origin, and the value of the other

pixels will be set as the value of background. In mathematics, we sign the operation that A eroded by B as A BΘ ：

{ }CZZ|(B) AA BΘ = ≠ ΦI （7）

In equation (7), Φ is a void set，A is the image we decided to process，B is a set of the structural elements. In another

word, the result of A eroded by B is a set which composed by the origins of all the structural elements, in which, the

values of the set B after being moved will not be superimposed with the value of background of the image A. [6]。

The values of the dilation structural elements and the corrosion structural elements are very important to chose, and they are the same. Fig.8 shows the image after closing operation. Compared with these three figures, we can safely come to a conclusion that after a series of processing, the finger-vein area and the background area have been separated obviously, and it has very important significance for further research.

Fig. 6 Image after being enhanced Fig.7 Image after binarization Fig.8 Image after closing operation

5. CONCLUSIONS

In this article, we analyzed the characteristics of the finger-vein image, and used the Gradient Inverse Weighted Smoothing method to denoise the image, we also improved the conventional high-frequency emphasis filtering algorithm, and proposed a new method which combined the conventional way with the histogram equalization algorithm to enhance the contrast of the image, and finally, we proposed a new sub-regional binarization approach together with the morphological image processing method to separate the finger-vein areas from the background areas. From the experiment results produced by MATLAB program, we can see that after a series of processing proposed above, the finger-vein areas can stand out from the original irregular shading image which provides an important reference for further research.

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