scholarly journals Biometric, Fingervein, Machine L Implementation of a Security System, Using Captured Fingervein, Applying the Concepts of Machine Learning

2020 ◽  
Vol 9 (04) ◽  
pp. 24994-25007
Author(s):  
Oyinloye Oghenerukevwe Elohor ◽  
Akinbohun Folake ◽  
Thompson Aderonke ◽  
Korede Bashir
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Accurate Solution ◽  
Great Promise ◽  
Biometric System ◽  
Finger Vein ◽  
Similarities And Differences ◽  
Vein Recognition ◽  
Finger Vein Recognition ◽  
Biometric Features

This work explores the field of biometric finger vein recognition – which is the identification of individuals using the unique vein patterns under their finger skins. This work also includes the implementation of an Android fingerprint biometric system using the Android Near InfraRed (NIR) module, which exists to show the similarities and differences between the two (fingervein and fingerprint) prevalent biometric features. This work thus confirms that finger vein recognition shows great promise as an accurate solution to modern society’s problem of automated personal authentication

Near Infrared Finger Vein Recognition Method Based on Subspace Projection Technology

2014 ◽  
Vol 1030-1032 ◽  
pp. 2382-2385 ◽  
Author(s):  
Lin Lin Fan ◽  
Hui Ma ◽  
Ke Jun Wang ◽  
Yong Liang Shen ◽  
Ying Shi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Recognition System ◽  
Critical Issue ◽  
Subspace Projection ◽  
Threshold Method ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

Finger vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. Finger vein recognition faces a number of challenges. One critical issue is the performance of finger vein recognition system. To overcome this problem, a finger vein recognition algorithm based on one kind of subspace projection technology is presented. Firstly, we use Kapur entropy threshold method to achieve the purpose of intercepting region of finger under contactless mode. Then the finger vein features were extracted by 2DPCA method. Finally, we used of nearest neighbor distance classifier for matching. The results indicate that the algorithm has good recognition performance.

A Near Infrared Finger Vein Recognition Approach Based on Wavelet Grayscale Surface Matching

2016 ◽  
Vol 53 (4) ◽  
pp. 041005 ◽  
Author(s):  
徐天扬 Xu Tianyang ◽  
惠晓威 Hui Xiaowei ◽  
林森 Lin Sen
Keyword(s):  
Near Infrared ◽  
Surface Matching ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

scholarly journals Finger Vein Recognition Using Vgg-16 Cnn Algorithm

2020 ◽  
Vol 9 (8) ◽  
pp. 112-114
Keyword(s):  
Error Rate ◽  
Data Security ◽  
System Security ◽  
Biometric System ◽  
Electronic Technology ◽  
Equal Error Rate ◽  
Biometric Recognition ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

With the advancement in the electronic technology, data identification and security is to be mainly considered as a factor in the security. Biometric recognition has been taken in to consideration for security purpose. Data security has to be done to prevent the system security from transmission of data by unauthorized users. Various authentications are taken in to consideration but most commonly focuses on finger print biometric system. Biometric recognition is taken in priority which is high safe and security oriented. Preprocessing, extraction and Equal Error rate are taken in to consideration. In this we are mainly focusing in finger vein authentication domains over the system implementation.

Enhancement of Finger-Vein Image by Vein Line Tracking and Adaptive Gabor Filtering for Finger-Vein Recognition

2011 ◽  
Vol 145 ◽  
pp. 219-223 ◽  
Author(s):  
So Ra Cho ◽  
Young Ho Park ◽  
Gi Pyo Nam ◽  
Kwang Youg Shin ◽  
Hyeon Chang Lee ◽  
...  
Keyword(s):  
Speaker Recognition ◽  
Near Infrared ◽  
Gabor Filter ◽  
Input Image ◽  
Skin Area ◽  
Original Image ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

Biometrics is the technology to identify a user by using the physiological or behavioral characteristics. Among the biometrics such as fingerprint, face, iris, and speaker recognition, finger-vein recognition has been widely used in various applications such as door access control, financial security, and user authentication of personal computer, due to its advantages such as small sized and low cost device, and difficulty of making fake vein image. Generally, a finger-vein system uses near-infrared (NIR) light illuminator and camera to acquire finger-vein images. However, it is difficult to obtain distinctive and clear finger-vein image due to skin scattering of illumination since the finger-vein exists inside of a finger. To solve these problems, we propose a new method of enhancing the quality of finger-vein image. This research is novel in the following three ways compared to previous works. First, the finger-vein lines of an input image are discriminated from the skin area by using local binarization, morphological operation, thinning and line tracing. Second, the direction of vein line is estimated based on the discriminated finger-vein line. And the thickness of finger-vein in an image is also estimated by considering both the discriminated finger-vein line and the corresponding position of finger-vein region in an original image. Third, the distinctiveness of finger-vein region in the original image is enhanced by applying an adaptive Gabor filter optimized to the measured direction and thickness of finger-vein area. Experimental results showed that the distinctiveness and consequent quality of finger-vein image are enhanced compared to that without the proposed method.

scholarly journals Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2296 ◽  
Author(s):  
Wan Kim ◽  
Jong Min Song ◽  
Kang Ryoung Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Image Acquisition ◽  
Multimodal Biometrics ◽  
Finger Vein ◽  
Nir Light ◽  
Vein Recognition ◽  
Finger Vein Recognition ◽  
Camera Sensor

Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods.

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