scholarly journals Extraction and Cross-Matching of Palm-Vein and Palmprint From the RGB and the NIR Spectrums for Identity Verification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 4005-4021 ◽  
Author(s):  
Sungchul Cho ◽  
Beom-Seok Oh ◽  
Kar-Ann Toh ◽  
Zhiping Lin
Keyword(s):  
Identity Verification ◽  
Palm Vein ◽  
Cross Matching

scholarly journals Identity Verification Through Palm Vein and Crease Texture

2005 ◽  
pp. 546-553 ◽  
Author(s):  
Kar-Ann Toh ◽  
How-Lung Eng ◽  
Yuen-Siong Choo ◽  
Yoon-Leon Cha ◽  
Wei-Yun Yau ◽  
...  
Keyword(s):  
Identity Verification ◽  
Palm Vein

scholarly journals Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5695
Author(s):  
Maciej Stanuch ◽  
Marek Wodzinski ◽  
Andrzej Skalski
Keyword(s):  
Neural Network ◽  
True Positive Rate ◽  
Equal Error Rate ◽  
Identity Verification ◽  
Positive Rate ◽  
Acceptance Set ◽  
Infrared Wavelengths ◽  
Palm Vein ◽  
Contact Free ◽  
Biometric Features

Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding supplementary modality while keeping the user experience at the same level. Palm vein systems are based on infrared wavelengths used for capturing images of users’ veins. It is both convenient for the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV wavelengths; the images are then processed by a deep convolutional neural network for extraction of biometric features and authentication of users. We tested the system in a verification scenario that consisted of checking if the images collected from the user contained the same biometric features as those in the database. The True Positive Rate (TPR) achieved by the system when the information from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal Error Rate (EER).

Routine Cross Matching of Blood for Major Upper GI Resectoins: Do we Need to Change the Current Practice?

Endoscopy ◽  
2006 ◽  
Vol 38 (11) ◽  
Author(s):  
PC Munipalle ◽  
JL Clements ◽  
BR Gopinath ◽  
PA Davis ◽  
YKS Viswanath
Keyword(s):  
Current Practice ◽  
Upper Gi ◽  
Cross Matching

Evaluation of the Automated Cross-Matching Instrument, ORTHO VISION, for Use in Blood Banks

2019 ◽  
Vol 9 (4) ◽  
pp. 218
Author(s):  
Mosae Koo ◽  
Jinsook Lim ◽  
Seon Yung Kim ◽  
Ji Myung Kim ◽  
Sun Hoe Koo ◽  
...  
Keyword(s):  
Blood Banks ◽  
Cross Matching

Hand Shape Coding: A Robust Approach for Human Identity Verification

2014 ◽  
Vol 1 (3) ◽  
pp. 8-17
Author(s):  
Shefali Sharma ◽  
◽  
Ashutosh Kumar Singh ◽  
Rajiv Saxena ◽  
◽  
...  
Keyword(s):  
Human Identity ◽  
Identity Verification ◽  
Robust Approach ◽  
Hand Shape ◽  
Shape Coding

scholarly journals Human Identification Using Contrast Enhanced Segmented Palm-Vein Images

2020 ◽  
Vol 993 ◽  
pp. 012097
Author(s):  
K Nandini ◽  
T. Surendran ◽  
S. Sobana ◽  
B. K. Chitra ◽  
T. Kalaiselvi
Keyword(s):  
Human Identification ◽  
Contrast Enhanced ◽  
Palm Vein

scholarly journals 2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

2021 ◽  
Author(s):  
Wei Jia ◽  
Wei Xia ◽  
Yang Zhao ◽  
Hai Min ◽  
Yan-Xiang Chen
Keyword(s):  
Deep Learning ◽  
Recognition Performance ◽  
Research Direction ◽  
Palmprint Recognition ◽  
Neural Architecture ◽  
Development Direction ◽  
Vein Recognition ◽  
Palm Vein ◽  
2D And 3D ◽  
Important Research Direction

AbstractPalmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

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