scholarly journals Convolutional Neural Network-based Finger Vein Recognition using Near Infrared Images

2018 ◽  
Author(s):  
Subha Fairuz ◽  
Mohamed Hadi Habaebi ◽  
Elsheikh Mohamed Ahmed Elsheikh ◽  
An Jalel Chebil
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Infrared Images ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

scholarly journals Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2296 ◽  
Author(s):  
Wan Kim ◽  
Jong Min Song ◽  
Kang Ryoung Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Image Acquisition ◽  
Multimodal Biometrics ◽  
Finger Vein ◽  
Nir Light ◽  
Vein Recognition ◽  
Finger Vein Recognition ◽  
Camera Sensor

Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods.

Finger Vein Recognition Using a Shallow Convolutional Neural Network

2021 ◽  
pp. 195-202
Author(s):  
Jiazhen Liu ◽  
Ziyan Chen ◽  
Kaiyang Zhao ◽  
Minjie Wang ◽  
Zhen Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

scholarly journals Finger Vein Recognition System Based on Convolutional Neural Network and Android

2021 ◽  
Vol 2078 (1) ◽  
pp. 012053
Author(s):  
Yangfeng Wang ◽  
Tao Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Rapid Development ◽  
Real Life ◽  
Recognition System ◽  
Finger Vein ◽  
Recognition Function ◽  
Vein Recognition ◽  
Finger Vein Recognition

Abstract With the rapid development of science and technology, biotechnology has developed rapidly. Among the many biometric technologies, finger vein technology has the characteristics of vitality, portability, and non-replicability, so it is considered to be the most promising biometric technology. However, the accuracy of finger vein recognition is affected by the collection device, the surrounding temperature and the algorithm. The flaws cannot be applied to real life on a large scale. This paper designs a finger vein recognition system based on convolutional neural network and Android, which mainly includes the following three parts. First, the system hardware includes the design of the acquisition device, the selection of the core development board and the display screen. Second, the design of the entire system software architecture is based on the MVVM architecture, which ensures low coupling of the program and is easy for later expansion and maintenance. The software includes collection function, recognition function and administrator function. Finally, a lightweight neural network is proposed for finger vein feature extraction, and proposed a storage method based on MMKV to meet the real-time performance of the system.

DS-CNN: A Pre-trained Xception Model based on Depth-wise Separable Convolutional Neural Network for Finger Vein Recognition

2021 ◽  
pp. 116288
Author(s):  
Kashif Shaheed ◽  
Aihua Mao ◽  
Imran Qureshi ◽  
Munish Kumar ◽  
Sumaira Hussain ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Finger Vein ◽  
Model Based ◽  
Vein Recognition ◽  
Finger Vein Recognition
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