New Finger-vein Recognition Method Based on Image Quality Assessment

2013 ◽  
Vol 7 (2) ◽  
pp. 374-365 ◽  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Recognition Method ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

A Novel Finger-Vein Recognition Based on Quality Assessment and Multi-Scale Histogram of Oriented Gradients Feature

2019 ◽  
Vol 15 (1) ◽  
pp. 100-115 ◽  
Author(s):  
Junying Zeng ◽  
Yao Chen ◽  
Yikui Zhai ◽  
Junying Gan ◽  
Wulin Feng ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Image Quality Assessment ◽  
Recognition System ◽  
Quality Score ◽  
Histogram Of Oriented Gradients ◽  
Test Results ◽  
Multi Scale ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

Inferior finger vein images would seriously alter the completion of recognition systems. A modern finger-vein recognition technique combined with image quality assessment is developed to overcome those drawbacks. By the quality assessment, this article can discard the inferior images and retain the superior images which are then transferred to the recognition system. Different from previous methods, this article assesses the quality features of the image for the purpose of distinguishing whether the image contains rich and stable vein characteristics. In light of this purpose, the quality assessment is implemented: first, the finger vein image is automatically annotated; second, the finger vein image is cut into image blocks to expand the training set; third, the average quality score of multiple image blocks from an image is the final quality score of the image in the course of testing. Next, the Histogram of Oriented Gradients (HOG) features are extracted from the four transformed high-quality sub-images, whose features are cascaded into the multi-scale HOG feature of an image. Finally, two modules, the quality assessment module using Convolutional Neural Networks (CNN) and finger vein recognition module which make full use of multi-scale HOG, are perfectly combined in this article. The test results have demonstrated that light-CNN can identifies inferior and superior images accurately and the multi-scale HOG is feasible and effective. What's more, this article can see the robustness of this combined method in this article.

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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