Finger Vein Pattern-based Authentication using Geometrical Properties of Corner Points

2017 ◽  
Author(s):  
Basavaraju R. ◽  
Chetana Hegde
Keyword(s):  
Vein Pattern ◽  
Geometrical Properties ◽  
Finger Vein ◽  
Corner Points

scholarly journals A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1885
Author(s):  
Qiong Yao ◽  
Dan Song ◽  
Xiang Xu ◽  
Kun Zou
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Spatial Information ◽  
Individual Recognition ◽  
Vein Pattern ◽  
Low Contrast ◽  
Feature Extraction Method ◽  
Structure Information ◽  
Finger Vein ◽  
Specific Implementation

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

Finger-vein pattern restoration with Direction-Variance-Boundary Constraint Search

2015 ◽  
Vol 46 ◽  
pp. 131-139 ◽  
Author(s):  
Tong Liu ◽  
Jianbin Xie ◽  
Wei Yan ◽  
Peiqin Li ◽  
Huanzhang Lu
Keyword(s):  
Vein Pattern ◽  
Finger Vein ◽  
Boundary Constraint

scholarly journals Finger Vein Recognition Using Optimal Partitioning Uniform Rotation Invariant LBP Descriptor

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Bang Chao Liu ◽  
Shan Juan Xie ◽  
Dong Sun Park
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Recognition Rate ◽  
Vein Pattern ◽  
Low Contrast ◽  
Finger Vein ◽  
Long Time ◽  
Vein Recognition ◽  
Lbp Descriptor ◽  
Selection Algorithms

As a promising biometric system, finger vein identification has been studied widely and many relevant researches have been proposed. However, it is hard to extract a satisfied finger vein pattern due to the various vein thickness, illumination, low contrast region, and noise existing. And most of the feature extraction algorithms rely on high-quality finger vein database and take a long time for a large dimensional feature vector. In this paper, we proposed two block selection methods which are based on the estimate of the amount of information in each block and the contribution of block location by looking at recognition rate of each block position to reduce feature extraction time and matching time. The specific approach is to find out some local finger vein areas with low-quality and noise, which will be useless for feature description. Local binary pattern (LBP) descriptors are proposed to extract the finger vein pattern feature. Two finger vein databases are taken to test our algorithm performance. Experimental results show that proposed block selection algorithms can reduce the feature vector dimensionality in a large extent.

scholarly journals Finger-Vein Verification Based on LSTM Recurrent Neural Networks

2019 ◽  
Vol 9 (8) ◽  
pp. 1687 ◽  
Author(s):  
Huafeng Qin ◽  
Peng Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Vein Pattern ◽  
Term Memory ◽  
Finger Vein ◽  
Spatial Dependencies ◽  
Long Short Term Memory

Finger-vein biometrics has been extensively investigated for personal verification. A challenge is that the finger-vein acquisition is affected by many factors, which results in many ambiguous regions in the finger-vein image. Generally, the separability between vein and background is poor in such regions. Despite recent advances in finger-vein pattern segmentation, current solutions still lack the robustness to extract finger-vein features from raw images because they do not take into account the complex spatial dependencies of vein pattern. This paper proposes a deep learning model to extract vein features by combining the Convolutional Neural Networks (CNN) model and Long Short-Term Memory (LSTM) model. Firstly, we automatically assign the label based on a combination of known state of the art handcrafted finger-vein image segmentation techniques, and generate various sequences for each labeled pixel along different directions. Secondly, several Stacked Convolutional Neural Networks and Long Short-Term Memory (SCNN-LSTM) models are independently trained on the resulting sequences. The outputs of various SCNN-LSTMs form a complementary and over-complete representation and are conjointly put into Probabilistic Support Vector Machine (P-SVM) to predict the probability of each pixel of being foreground (i.e., vein pixel) given several sequences centered on it. Thirdly, we propose a supervised encoding scheme to extract the binary vein texture. A threshold is automatically computed by taking into account the maximal separation between the inter-class distance and the intra-class distance. In our approach, the CNN learns robust features for vein texture pattern representation and LSTM stores the complex spatial dependencies of vein patterns. So, the pixels in any region of a test image can then be classified effectively. In addition, the supervised information is employed to encode the vein patterns, so the resulting encoding images contain more discriminating features. The experimental results on one public finger-vein database show that the proposed approach significantly improves the finger-vein verification accuracy.

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