基于PCA-RBF神经网络的手指静脉识别A Finger Vein Recognition Method Based on PCA-RBF Neural Network

2012 ◽  
Vol 02 (04) ◽  
pp. 23-27
Author(s):  
余 成波
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Recognition Method ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

A Finger Vein Recognition Method Based on PCA-RBF Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 1653-1658 ◽  
Author(s):  
Cheng Bo Yu ◽  
Jun Tan ◽  
Lei Yu ◽  
Yin Li Tian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Back Propagation ◽  
Principal Component ◽  
Training Sample ◽  
Finger Vein ◽  
Simpler Algorithm ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper puts forward a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. The result shows that PCA-RBF has faster training speed, simpler algorithm and higher recognition rate.

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

Finger Vein Recognition Based on Stable and Discriminative Superpixels

2016 ◽  
Vol 30 (06) ◽  
pp. 1650015 ◽  
Author(s):  
Lizhen Zhou ◽  
Gongping Yang ◽  
Yilong Yin ◽  
Lu Yang ◽  
Kuikui Wang
Keyword(s):  
Recognition Performance ◽  
Fusion Method ◽  
Recognition Method ◽  
Biometric Technology ◽  
Training Images ◽  
Finger Vein ◽  
Vein Recognition ◽  
Matching Score ◽  
Finger Vein Recognition ◽  
Better Than

Finger vein pattern, as a promising hand-based biometric technology, has been well studied in recent years. In this paper, a new superpixel-based finger vein recognition method is presented. In the proposed method, we develop two types of effective superpixels, i.e. stable superpixel and discriminative superpixel to represent finger vein image and these superpixels are expected to play different roles in matching stage. In detail, the stable and discriminative superpixels are firstly learned from the training images for each enrolled class. When verifying a testing image, we just compare the superpixels at the same location as the two types of superpixels in template. Then, the two types of superpixels are combined utilizing a reversible weight-based fusion method in score level. Additionally, to further improve the recognition performance, we explore the superpixel context feature (SPCF). For each superpixel the SPCF is obtained by comparing the current superpixel with its surrounding neighbors. In the final matching stage, we integrate the matching score of two types of superpixels and it of the SPCF using the weighted SUM fusion method. The experimental results on two open finger vein databases, i.e. PolyU and SDUMLA-FV, show that our method not only performs better than the existing superpixel-based method, but also has advantages in comparison with some traditional ones.

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