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.

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 Classification of Multi-spectral Landsat Satellite Imagery Dataset using Feature Extraction Methods

2020 ◽  
Vol 9 (4) ◽  
Keyword(s):  
Land Use ◽  
Feature Extraction ◽  
Satellite Imagery ◽  
Extraction Method ◽  
Spatial Information ◽  
Extraction Methods ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Landsat Satellite ◽  
Spatial Maps

The feature extraction of multi-spectral Landsat satellite imagery Dataset is essential for vegetation monitoring, urban planning, change assessment, and other land-use applications. The spatial information provided by Remote sensing satellite imagery data is helpful for planning and decision-making policies. In the present study, classify the features of the multi-spectral Landsat satellite imagery dataset in different periods using the feature extraction method, and is produced the spatial maps of the study area. The study is to analyze the appropriate method of feature extraction for classifying the orchards, vegetation, rangeland, agricultural land, wetland, water body, and urban land using multi-temporal satellite dataset. In this study, use the three feature extraction methods are support vector machine (SVM), minimum distance (MD), and Maximum likelihood classifier (MLC) for supervised pixel-based classification using medium resolution (30 m) satellite dataset. The accuracy of feature extraction method is performed by the MLC (86.29% and 93% in the year 2003 and 2017) and SVM (86.37% and 90% in the year 2003 and 2017). The result of the presented study shows MLC and SVM classifier performs similar results but better than MD classifier for land-use/cover features classification. The classified spatial maps provide the essential spatial information for land-use changes occurred during the last 15 years (2003 to 2017).

Skeleton-Based Feature Extraction Method for Two-Dimensional Potential Energy

2010 ◽  
Vol 139-141 ◽  
pp. 2051-2054 ◽  
Author(s):  
Xue Song Chen ◽  
Cheng Wang ◽  
Xue Jun Xu ◽  
Hong Bo Zhu ◽  
Shao Hua Jiang
Keyword(s):  
Feature Extraction ◽  
Potential Energy ◽  
Extraction Method ◽  
Target Recognition ◽  
Recognition System ◽  
Image Feature ◽  
Feature Extraction Method ◽  
Structure Information ◽  
The Matrix ◽  
Point Projection

A good feature extraction method can improve the performance of pattern recognition system or classification system. Using potential energy theory into binary image feature extraction and feature store is a new method for image processing. The skeleton can be better display the whole features of the object. In target recognition system, using potential energy of skeleton-point projection into the plane coordinate system. The method can be better to show a skeleton in the structural feature. In addition, it can better avoid the matrix storage redundancy. In all energy projection method, potential energy projection is better shown its superiority in the structure information, the time of consumption and the storage space. The skeleton potential energy can be used in target recognition and target classification field and so on.

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