• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1766-1784
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• 2014

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.261-263
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• 2005

In this paper, we implement an identification system using the vein image of the hand. The vein pattern is obtained in the grey-scale 2D image through the infrared-red imaging from back of the hand. Since the frame has lack of clearance, we use some enhancing methods such as the complement, addition, and multiplication to the image to increase the contrast. After Wiener filtering for smoothness of the vein pattern, we transform the image into the binary image with mean function. The binarized image is session thinned and the cross-points in the vein tree are obtained by calculating the number of pixels connected because the image is shaped as a tree. We choose the point and find the nearest to the center if it has majority, where we find the two end points of the selected line. We can get the angle between the two lines joined at the cross-point and store its coordinates, angle, and label the values. The values are used as the feature vector of the vein pattern. This procedure is similar to the human cognition sequences. It is shown that the proposed method is simple for the vein recognition.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.275-284
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• 2008

With increases in recent security requirements, biometric technology such as fingerprints, faces and iris recognitions have been widely used in many applications including door access control, personal authentication for computers, internet banking, automatic teller machines and border-crossing controls. Finger vein recognition uses the unique patterns of finger veins in order to identify individuals at a high level of accuracy. This paper proposes new device and methods for touchless finger vein recognition. This research presents the following five advantages compared to previous works. First, by using a minimal guiding structure for the finger tip, side and the back of finger, we were able to obtain touchless finger vein images without causing much inconvenience to user. Second, by using a hot mirror, which was slanted at the angle of 45 degrees in front of the camera, we were able to reduce the depth of the capturing device. Consequently, it would be possible to use the device in many applications having size limitations such as mobile phones. Third, we used the holistic texture information of the finger veins based on a LBP (Local Binary Pattern) without needing to extract accurate finger vein regions. By using this method, we were able to reduce the effect of non-uniform illumination including shaded and highly saturated areas. Fourth, we enhanced recognition performance by excluding non-finger vein regions. Fifth, when matching the extracted finger vein code with the enrolled one, by using the bit-shift in both the horizontal and vertical directions, we could reduce the authentic variations caused by the translation and rotation of finger. Experimental results showed that the EER (Equal Error Rate) was 0.07423% and the total processing time was 91.4ms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.915-916
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• 2013

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.365-367
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• 2016

Recently, the importance of personal information security has emerged. In particular, the importance of biometrics such as voice recognition, fingerprint recognition, face recognition has been highlighted in various fields such as access control, banking security, personal PC security and so on. But if such conventional security techniques are likely to be recognized by an external factor failure. In contrast, if the vein recognition is impossible to copy and counterfeit. Therefore less likely to fail due to external factors has the advantage. In this paper, propose a preprocessing method to improve on the existing vein pattern recognition with high reliability than existing biometric technologies.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3022-3024
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• 2000

Biometrics is the electronic recognition of individuals achieved through a process of extracting, and then verifying, features which are unique to that individual. This field is rapidly evolving technology that has to be widely adopted in a broad range of applications. Many methods have been studied such as extraction of the facial features, the voice, the vein and even a person's signature. Among biometrics, a hand veins provide large, robust, stable, hidden biometric features. Hand vein patterns have been proven to be absolutely unique by Cambridge Consultants Ltd. Because of this advantage, hand vein recognition are recently developing field in the field of a security.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.300-302
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• 2022

Palm vein identification has attracted attention due to its distinct characteristics and excellent recognition accuracy. However, many contactless palm vein identification systems suffer from the issue of having low-quality palm images, resulting in degradation of recognition accuracy. This paper proposes the use of U-Net architecture to correctly segment the vascular blood vessel from palm images. Attention gate mechanism and residual block are also utilized to effectively learn the crucial features of a specific segmentation task. The experiments were conducted on CASIA dataset. Hessian-based Jerman filtering method is applied to label the palm vein patterns from the original images, then the network is trained to segment the palm vein features from the background noise. The proposed method has obtained 96.24 IoU coefficient and 98.09 dice coefficient.

• Journal of Satellite, Information and Communications
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• v.11 no.1
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• pp.16-20
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• 2016

In this paper, we analyze the characteristics and ATM applications for fingerprint, iris, vein recognition technology that can be applied to the ATM to reinforce security. Describe the important requirements to be considered when introducing a biometric authentication in the ATM, and were compared to each biometric authentication scheme based on these requirements. Fingerprint authentication has limitations in accuracy, iris recognition is a big weakness in user convenience, whereas vein recognition has the advantage of being hygienic, yet excellent accuracy and secrecy. Vein authentication approach is expected to be expanded to apply to ATM due to the many advantages.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.423-430
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• 2013

Recently, the personal identification technologies using vein pattern of back of the hand, palm, and finger have been developed actively because it has the advantage that the vein blood vessel in the body is impossible to damage, make a replication and forge. However, it is difficult to extract clearly the vein region from captured vein images through common image prcessing based region segmentation method, because of the light scattering and non-uniform internal tissue by skin layer and inside layer skeleton, etc. Especially, it takes a long time for processing time and makes a discontinuity of blood vessel just in a image because it has non-uniform illumination due to use a locally different adaptive threshold for the binarization of acquired finger-vein image. To solve this problem, we propose illumination normalization based fast method for extracting the finger-vein region. The proposed method has advantages compared to the previous methods as follows. Firstly, for remove a non-uniform illumination of the captured vein image, we obtain a illumination component of the captured vein image by using a low-pass filter. Secondly, by extracting the finger-vein path using one time binarization of a single threshold selection, we were able to reduce the processing time. Through experimental results, we confirmed that the accuracy of extracting the finger-vein region was increased and the processing time was shortened than prior methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.327-334
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• 2024

In this paper, we developed a system with a near-infrared LED light source with a wavelength of 950nm to acquire palm vein images and a white LED light source to acquire palm creases based on Raspberry Pi. In addition, we implemented a unique pattern-extractable image processing technology that can prevent counterfeiting and enhance security of mixed creases and palmprints through image pre-processing (Gray scaling, Histogram Equalization, Blurring, Thresholding, Thinning) for the acquired vein and palm images, and secured a source technology that can be used in a security-enhanced system.