Finger vein recognition using Gabor filter and Support Vector Machine

2014 ◽  
Author(s):  
Souad Khellat-kihel ◽  
Reza abrishambaf ◽  
Nuno Cardoso ◽  
Joao Monteiro ◽  
Mohamed Benyettou
Keyword(s):  
Support Vector Machine ◽  
Gabor Filter ◽  
Support Vector ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

scholarly journals Adaptive Learning Gabor Filter for Finger-Vein Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 159821-159830 ◽  
Author(s):  
Yakun Zhang ◽  
Weijun Li ◽  
Liping Zhang ◽  
Xin Ning ◽  
Linjun Sun ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Gabor Filter ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

Finger Vein Recognition Based on Gabor Filter

2013 ◽  
pp. 827-834 ◽  
Author(s):  
Hong Zhang ◽  
Zhi Liu ◽  
Qijun Zhao ◽  
Congcong Zhang ◽  
Dandan Fan
Keyword(s):  
Gabor Filter ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

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 Finger-Vein Recognition Using Bidirectional Feature Extraction and Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiyong Tao ◽  
Xinru Zhou ◽  
Zhixue Xu ◽  
Sen Lin ◽  
Yalei Hu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Feature Recognition ◽  
Recognition Performance ◽  
Support Vector ◽  
Position Information ◽  
Finger Vein ◽  
Security Research ◽  
Vein Recognition ◽  
Finger Vein Recognition

Accuracy and efficiency are essential topics in the current biometric feature recognition and security research. This paper proposes a deep neural network using bidirectional feature extraction and transfer learning to improve finger-vein recognition performance. Above all, we make a new finger-vein database with the opposite position information of the original one and adopt transfer learning to make the network suitable for our overall recognition framework. Next, the feature extractor is constructed by adjusting the unidirectional database’s parameters, capturing vein features from top to bottom and vice versa. Correspondingly, we concatenate the above two features to form the finger-veins’ bidirectional features, which are trained and classified by Support Vector Machines (SVM) to realize recognition. Experiments are conducted on the Malaysian Polytechnic University’s published database (FV-USM) and finger veins of Signal and Information Processing Laboratory (FV-SIPL). The accuracy of our proposed algorithm reaches 99.67% and 99.31%, which is significantly higher than the unidirectional recognition under each database. Compared with the algorithms cited in this paper, our proposed model based on bidirectional feature enjoys higher accuracy, faster recognition speed than the state-of-the-art frameworks, and excellent practical value.

scholarly journals Weber Local Descriptors With Variable Curvature Gabor Filter for Finger Vein Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 108261-108277 ◽  
Author(s):  
Huabin Wang ◽  
Mengli Du ◽  
Jian Zhou ◽  
Liang Tao
Keyword(s):  
Gabor Filter ◽  
Local Descriptors ◽  
Finger Vein ◽  
Variable Curvature ◽  
Vein Recognition ◽  
Finger Vein Recognition

scholarly journals Detecting Fake Finger-Vein Data Using Remote Photoplethysmography

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1016 ◽  
Author(s):  
Jin Yeong Bok ◽  
Kun Ha Suh ◽  
Eui Chul Lee
Keyword(s):  
Main Idea ◽  
Arterial Blood Flow ◽  
Experimental Result ◽  
Support Vector ◽  
Finger Vein ◽  
Arterial Blood ◽  
Remote Photoplethysmography ◽  
Vein Recognition ◽  
Biometric Information ◽  
Finger Vein Recognition

Today, biometrics is being widely used in various fields. Finger-vein is a type of biometric information and is based on finger-vein patterns unique to each individual. Various spoofing attacks have recently become a threat to biometric systems. A spoofing attack is defined as an unauthorized user attempting to deceive a system by presenting fake samples of registered biometric information. Generally, finger-vein recognition, using blood vessel characteristics inside the skin, is known to be more difficult when producing counterfeit samples than other biometrics, but several spoofing attacks have still been reported. To prevent spoofing attacks, conventional finger-vein recognition systems mainly use the difference in texture information between real and fake images, but such information may appear different depending on the camera. Therefore, we propose a method that can detect forged finger-vein independently of a camera by using remote photoplethysmography. Our main idea is to get the vital sign of arterial blood flow, a biometric measure indicating life. In this paper, we selected the frequency spectrum of time domain signal obtained from a video, as the feature, and then classified data as real or fake using the support vector machine classifier. Consequently, the accuracy of the experimental result was about 96.46%.

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