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

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.

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