A Survey of Vein Recognition Techniques

Luo, Hao; Yu, Faxin; Pan, Jeng-Shyang; Chu, Shu-Chuan; Tsai, Pei‐Wei

doi:10.3923/itj.2010.1142.1149

Cited by 31 publications

(21 citation statements)

References 13 publications

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“…An assessment of biometrics is exposed in Table 1. Preferably, any physiological or behavioral characteristic can't be a biometric quality except if it fulfills the accompanying criteria [5] [24]. 2) Universality −It indicates that each and every person has the unique characteristics, which cannot be reproduced.…”

Section: A Biometric Factors Biometrics Has Seven Factors: 1) Uniquementioning

confidence: 99%

“…3, the four Main components of this architecture are as follows [5]. Fig.3 General Framework of palm and finger vein processing [5]. 1) Enrolment: This is a first process that is very crucial and important for membership.…”

Section: B General Framework Of Vein Recognitionmentioning

confidence: 99%

“…Once the user's hand is held over the scanner, the vein inside the Palm of the user appears black and then it is registered as an "identity card" in a database. Finally, it's matched with previously registered patterns of people in the existing [3] [4] [5].…”

Section: Introductionmentioning

confidence: 99%

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Palm Vein Recognition System for Human Authentication: A Review

Parihar¹

2019

IJRASET

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Palm vein technology is the upcoming technology that is extremely secure and unique because one cannot forged the pattern of veins. No doubt there are numerous biometric recognition techniques such as palm vein, iris, fingerprint, retina etc., which will be used as biometric identifiers to confirm the human's identity, but those methods didn't produce satisfactory results, so we need a technology that secure our information more efficiently from an unauthorized access. This paper presents an analysis of palm vein pattern recognition, its development, principle & general framework of palm and finger vein recognition, its techniques, its applications, and advantages of using this technology.

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Section: A Biometric Factors Biometrics Has Seven Factors: 1) Uniquementioning

confidence: 99%

Section: B General Framework Of Vein Recognitionmentioning

confidence: 99%

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Palm Vein Recognition System for Human Authentication: A Review

Parihar¹

2019

IJRASET

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“…在公安、金融等对身份识别要求较高的行业，急需高精度、支持大数据量的识别方法。近年来，基于手掌静脉的身份识别技术由于其极度难以伪造，支持活体特征检测等特点而成为了新的研究热点。现有的手掌静脉识别方法，主要是基于单幅的二维(2D)手掌静脉近红外图像进行识别。在获取手掌静脉二维图像后，通过提取静脉的几何结构特征点如分叉点、端点等作为特征，或直接使用图像特征点提取算法对手掌静脉进行匹配和识别。 Wang 等 [1][2] 利用尺度不变特征变换 (SIFT)描述符作为静脉血管图像的特征进行，通过去除噪点等匹配策略对手掌静脉图像直接进行识别。Cross 等 [3] 将静脉血管依据位置和角度信息分割成不同的向量作为识别特征。Liu 等 [4] 验证了多样点间距矩阵方法的有效性。Rothaus 等 [5] 则提出了利用分割动脉和静脉血管结构的识别方法。这些方法大部分都只利用了手背或手掌的二维静脉图像 [6] ，而忽视了手部静脉血管的深度信息。由于手掌静脉图像包含的纹理较为简单，且近红外图像只具有灰度信息，因此二维图像本身携带的特征信息较少，从而导致上述方法提取的特征信息维度低、数目少，在面对大数据集的认证时可能出现特征不足的问题。此外，在拍摄二维手掌静脉图像时，由于人手掌的姿态并不固定，因此当手掌倾斜或旋转时，手掌静脉在二维图像上的投影也随之改变，从而导致误拒率过高的问题，即便在理想实验条件下，上述方法发布的实验结果里误拒率也高达 5.5% [7] 。针对以上两个问题，提出了基于手掌静脉三维(3D)点云匹配的手掌静脉识别方法。主要思路是通过双 [8][9][10][11] ，本文选择了内核相关性分析 (KC) [12][13] 方法并对其加以改进，使其更适合手掌静脉的三维点云匹配。KC 方法的优点在于不需要提前采集数据集对分…”

Section: -unclassified

Hand Vein Recognition Based on Three Dimensional Point Clouds Matching

Qishen¹,

Ya²,

Xiaoming³

et al. 2015

光学学报

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In order to solve the problem of high false rejection rate and not supporting large data base registration in existing hand vein recognition system, a binocular stereoscopic vision device for hand vein three dimensional (3D) point can reconstruction is proposed, along with the hand vein 3D point cloud matching algorithm. The hand is lighted by an 850 nm light emitting diode (LED) light source, binocular images for 3D reconstruction are obtained by the stereo cameras. The hand vein′s spatial structure is described by hand veins feature, an optimized kernel correlation analysis approach is proposed for 3D point cloud matching. Experimental results of 200 different point clouds data show the proposed system is feasible and effective, the recognition rate is 98% , false rejection rate is 2% and the false accept rate is 0% , the feature′ s dimension is ranged from 8000 to 12000, which is higher than that of scale invariant feature transform (SIFT). The proposed system provides a possibility for large database recognition.

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“…It has drawn many researchers' attention [1]- [3]. In existing methods, most of them extract hand vein information from one single hand-dorsal image, which will cause deficiency of hand vein's depth information.…”

Section: Introductionmentioning

confidence: 99%

A 3D interpolation method in repairing hand vascular tree for vein recognition

Zhu

Yan

et al. 2013

2013 IEEE International Conference on Imaging Systems and Techniques (IST)

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Hand vein recognition is one of the most widely used methods for authentication. However, since the restriction of fixed hand posture and the loss of vein's depth message in traditional 2D vein recognition will cause a higher false rejection rate (FRR), many researchers have focused on the 3D hand vein recognition. For vein recognition, the knowledge of the vascular tree's topology and symbolic description of its feature points will be essential, while by using the common 3D reconstruction methods the vein data obtained is sparse and not enough to describe the topology of hand vein. In this paper, a computational framework to develop vascular tree interpolation with minimum human intervention to repair the possible broken vessels is proposed, which comprises three main parts including labeling point cloud into different clusters, finding pairs of interpolation points and the process of interpolation. This achieves good performance on 18 sets of hand vein point clouds.

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A Survey of Vein Recognition Techniques

Cited by 31 publications

References 13 publications

Palm Vein Recognition System for Human Authentication: A Review

Palm Vein Recognition System for Human Authentication: A Review

Hand Vein Recognition Based on Three Dimensional Point Clouds Matching

A 3D interpolation method in repairing hand vascular tree for vein recognition

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