Dorsal Hand Vein Recognition Based on Improved Bag of Visual Words Model

2017 ◽  
pp. 203-212
Author(s):  
Yiding Wang ◽  
Shan Dong
Keyword(s):  
Bag Of Visual Words ◽  
Visual Words ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Vein Recognition ◽  
Dorsal Hand Vein ◽  
Dorsal Hand Vein Recognition

scholarly journals Recognition of Dorsal Hand Vein Based Bit Planes and Block Mutual Information

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3718 ◽  
Author(s):  
Yiding Wang ◽  
Heng Cao ◽  
Xiaochen Jiang ◽  
Yuanyan Tang
Keyword(s):  
Mutual Information ◽  
Recognition Rate ◽  
Scale Invariant ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Bit Plane ◽  
Vein Recognition ◽  
Dorsal Hand Vein ◽  
Bit Planes ◽  
Dorsal Hand Vein Recognition

The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was put forward based on bit plane and block mutual information in this paper. Firstly, the input gray image of dorsal hand vein was converted to eight-bit planes to overcome the interference of brightness inside the higher bit planes and the interference of noise inside the lower bit planes. Secondly, the texture of each bit plane of dorsal hand vein was described by a block method and the mutual information between blocks was calculated as texture features by three kinds of modes to solve the problem of rotation and size. Finally, the experiments cross-device were carried out. One device was used to be registered, the other was used to recognize. Compared with the SIFT (Scale-invariant feature transform, SIFT) algorithm, the new algorithm can increase the recognition rate of dorsal hand vein from 86.60% to 93.33%.

scholarly journals MULTISPECTRAL DORSAL HAND VEIN RECOGNITION BASED ON LOCAL LINE BINARY PATTERN

2018 ◽  
Vol 11 (2) ◽  
pp. 95 ◽  
Author(s):  
Fransisca J Pontoh ◽  
Jayanti Yusmah Sari ◽  
Amil A Ilham ◽  
Ingrid Nurtanio
Keyword(s):  
Feature Extraction ◽  
Low Cost ◽  
Experimental Result ◽  
Person Identification ◽  
Feature Extraction Method ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Vein Recognition ◽  
Dorsal Hand Vein ◽  
Dorsal Hand Vein Recognition

Nowadays, dorsal hand vein recognition is one of the most recent multispectral biometrics technologies used for the person identification/authentication. Looking into another biometrics system, dorsal hand vein biometrics system has been popular because of the privilege: false duplicity, hygienic, static, and convenient. The most challenging phase in a biometric system is feature extraction phase. In this research, feature extraction method called Local Line Binary Pattern (LLBP) has been explored and implemented. We have used this method to our 300 dorsal hand vein images obtained from 50 persons using a low-cost infrared webcam. In recognition step, the adaptation fuzzy k-NN classifier is to evaluate the efficiency of the proposed approach is feasible and effective for dorsal hand vein recognition. The experimental result showed that LLBP method is reliable for feature extraction on dorsal hand vein recognition with a recognition accuracy up to 98%.

Dorsal Hand Vein Recognition Based on Transfer Learning with Fusion of LBP Feature

2021 ◽  
pp. 221-230
Author(s):  
Gaojie Gu ◽  
Peirui Bai ◽  
Hui Li ◽  
Qingyi Liu ◽  
Chao Han ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Vein Recognition ◽  
Dorsal Hand Vein ◽  
Dorsal Hand Vein Recognition

scholarly journals An Efficient Dorsal Hand Vein Recognition Security System using Arduino and GSM Technology

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1577-1581
Keyword(s):  
Neural Network ◽  
Median Filter ◽  
Region Of Interest ◽  
Biometric Verification ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Line Tracking ◽  
Vein Recognition ◽  
Dorsal Hand Vein ◽  
Dorsal Hand Vein Recognition

With the onset of maximum power, modest figuring and more prominent unpredictability, biometric verification has turned out to be conceivable at each scale in light of its more secure nature and furthermore easy to use conduct. Compare to other biometrics, vein biometric is a decent verification characteristic among others. The dorsal hand vein recognition is an emerging biometric procedure which is utilized for verification purposes in many applications. In this work preprocessing is done by median filter and region of interest such as veins separated from the muscles and bones through adaptive Kmeans clustering algorithm.The proposed method extracts the dorsal hand vein pattern features by using LBP and Repeated Line Tracking algorithm.Finally recognition and authentication is done using Artificial Neural Network. Arduino and GSM technology is used in this work to set security preference for the particular user.In order to validate the proposed work , a total of 480 images of dorsal hand veins is involved in this work. In a comparison with four existingverification algorithms, the proposed method achieves thehighest accuracy with lowest error rate.

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