scholarly journals Dorsal Hand Vein Image Enhancement for Improve Recognition Rate Based on SIFT Keypoint Matching

2013 ◽  
Author(s):  
Sathaporn Chanthamongkol ◽  
Boonchana Purahong ◽  
Attasit Lasakul
Keyword(s):  
Image Enhancement ◽  
Recognition Rate ◽  
Keypoint Matching ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Dorsal Hand Vein

scholarly journals Recognition of Dorsal Hand Vein Based Bit Planes and Block Mutual Information

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3718 ◽  
Author(s):  
Yiding Wang ◽  
Heng Cao ◽  
Xiaochen Jiang ◽  
Yuanyan Tang
Keyword(s):  
Mutual Information ◽  
Recognition Rate ◽  
Scale Invariant ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Bit Plane ◽  
Vein Recognition ◽  
Dorsal Hand Vein ◽  
Bit Planes ◽  
Dorsal Hand Vein Recognition

The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was put forward based on bit plane and block mutual information in this paper. Firstly, the input gray image of dorsal hand vein was converted to eight-bit planes to overcome the interference of brightness inside the higher bit planes and the interference of noise inside the lower bit planes. Secondly, the texture of each bit plane of dorsal hand vein was described by a block method and the mutual information between blocks was calculated as texture features by three kinds of modes to solve the problem of rotation and size. Finally, the experiments cross-device were carried out. One device was used to be registered, the other was used to recognize. Compared with the SIFT (Scale-invariant feature transform, SIFT) algorithm, the new algorithm can increase the recognition rate of dorsal hand vein from 86.60% to 93.33%.

scholarly journals Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6445
Author(s):  
Marlina Yakno ◽  
Junita Mohamad-Saleh ◽  
Mohd Zamri Ibrahim
Keyword(s):  
Image Enhancement ◽  
Similarity Index ◽  
Weighted Average ◽  
Weighted Averaging ◽  
Grey Level ◽  
Enhancement Technique ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Dorsal Hand Vein ◽  
Fuzzy Adaptive

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.

Cross-device hand vein recognition based on improved SIFT

2018 ◽  
Vol 16 (02) ◽  
pp. 1840010 ◽  
Author(s):  
Yiding Wang ◽  
Xuan Zheng
Keyword(s):  
Recognition Rate ◽  
Light Sources ◽  
Neighborhood Structure ◽  
Sift Algorithm ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Imaging Camera ◽  
Vein Recognition ◽  
Dorsal Hand Vein ◽  
Angle Of Rotation

The recognition rate of SIFT algorithm in single hand vein database has been as high as 99.5%. But with the development of Internet-plus technology, the demand for distributed systems becomes more and more significant. However, the problem of picture quality caused by cross-device makes the intraclass variations larger. For example, when gathering the dorsal hand vein images, subtle changes in relative distance and orientation among the imaging camera, the illumination LED arrays and the different location of users’ hand, as well as shielding by the external housing box from ambient light sources and so on, these will make large difference to one person’s hand images. So, including the contrast, the lightness, the shifting, the angle of rotation, the size and so on, these differences make it possible to use some traditional methods to recognize dorsal hand vein with a low recognition rate of less than 50%. Therefore, based on the traditional SIFT, this paper optimized the scale factor [Formula: see text], extreme searching neighborhood structure and matching threshold [Formula: see text]. It can be seen that the cross-device hand vein feature is more robust, and the recognition rate reached an average of 88.5%.

The Influence of Age on Dorsal Hand Vein Responsiveness to Epinephrine

1987 ◽  
Vol XXXI (3) ◽  
pp. 141
Author(s):  
S. A. MARTIN ◽  
S. ALEXIEVA ◽  
S. G. CARRUTHERS
Keyword(s):  
Dorsal Hand ◽  
Hand Vein ◽  
Dorsal Hand Vein

Robust Authentication Using Dorsal Hand Vein Images

2019 ◽  
Vol 34 (2) ◽  
pp. 25-35 ◽  
Author(s):  
Parul Arora ◽  
Smriti Srivastava ◽  
Madasu Hanmandlu ◽  
Sandeep Bhargava
Keyword(s):  
Dorsal Hand ◽  
Hand Vein ◽  
Dorsal Hand Vein

scholarly journals Disproportionate Decrease in α- Compared With β-Adrenergic Sensitivity in the Dorsal Hand Vein in Pregnancy Favors Vasodilation

Circulation ◽  
2002 ◽  
Vol 106 (9) ◽  
pp. 1116-1120 ◽  
Author(s):  
Ruth Landau ◽  
Victor Dishy ◽  
Alastair J.J. Wood ◽  
C. Michael Stein ◽  
Richard M. Smiley
Keyword(s):  
Dorsal Hand ◽  
Hand Vein ◽  
Dorsal Hand Vein ◽  
In Pregnancy
Sign in / Sign up

Export Citation Format

Share Document