Finger Vein Recognition Method Based on Gaussian Low-Pass Filter and Direction Detection

2014 ◽  
Author(s):  
Lin You ◽  
Lei Sun ◽  
Fenghai Li ◽  
Xueshuang Li
Keyword(s):  
Recognition Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Finger Vein ◽  
Low Pass ◽  
Direction Detection ◽  
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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