A Region Growing Method Based on Statistical Attributes of Infrared Images for Finger Vein Pattern Extraction

2013 ◽  
pp. 1-15
Author(s):  
M. Vlachos ◽  
E. Dermatas
Keyword(s):  
Region Growing ◽  
Infrared Images ◽  
Pattern Extraction ◽  
Vein Pattern ◽  
Finger Vein

scholarly journals Finger Vein Segmentation from Infrared Images Based on a Modified Separable Mumford Shah Model and Local Entropy Thresholding

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Marios Vlachos ◽  
Evangelos Dermatas
Keyword(s):  
Image Enhancement ◽  
Local Application ◽  
Local Entropy ◽  
Infrared Images ◽  
Vein Pattern ◽  
Finger Vein ◽  
Small Window ◽  
Minimization Procedure ◽  
Novel Method ◽  
Computationally Intensive

A novel method for finger vein pattern extraction from infrared images is presented. This method involves four steps: preprocessing which performs local normalization of the image intensity, image enhancement, image segmentation, and finally postprocessing for image cleaning. In the image enhancement step, an image which will be both smooth and similar to the original is sought. The enhanced image is obtained by minimizing the objective function of a modified separable Mumford Shah Model. Since, this minimization procedure is computationally intensive for large images, a local application of the Mumford Shah Model in small window neighborhoods is proposed. The finger veins are located in concave nonsmooth regions and, so, in order to distinct them from the other tissue parts, all the differences between the smooth neighborhoods, obtained by the local application of the model, and the corresponding windows of the original image are added. After that, veins in the enhanced image have been sufficiently emphasized. Thus, after image enhancement, an accurate segmentation can be obtained readily by a local entropy thresholding method. Finally, the resulted binary image may suffer from some misclassifications and, so, a postprocessing step is performed in order to extract a robust finger vein pattern.

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.

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