Biometrie hand vein estimation using bloodstream filtration and fuzzy e-means

Kolda, Lukas; Krejcar, Ondřej

doi:10.1109/fuzz-ieee.2017.8015736

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…In this section, the extraction of hand and bloodstream data is discussed. The entire system commences with obtaining the image of the hand, and then that of the bloodstream (vascular structure) [48,49]. Next, background noise is removed from the images to make it fit for further processing.…”

Section: System Descriptionmentioning

confidence: 99%

Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification

Kolda

Krejcar

Selamat

et al. 2019

Sensors

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Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation team, which produced 280 hand images. The core of this paper lies in evaluating individual metrics, which are functions of frequency comparison of the double type faults with the EER (Equal Error Rate) values. The lowest value was measured for the case of the modified Hausdorff distance metric - Maximally Helicity Violating (MHV). Furthermore, for the verified biometric characteristics (Hamming distance and MHV), appropriate and suitable metrics have been proposed and experimented to optimize system precision. Thus, the EER value for the designed multi-biometric system in the context of this work was found to be 5%, which proves that metrics consolidation increases the precision of the multi-biometric system. Algorithms used for the proposed multi-biometric device shows that the individual metrics exhibit significant accuracy but perform better on consolidation, with a few shortcomings.

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Section: System Descriptionmentioning

confidence: 99%

Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification

Kolda

Krejcar

Selamat

et al. 2019

Sensors

Self Cite

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“…Compared with other extraction methods such as the rectangular method [19], the method of the maximum inscribed circle has many advantages. Firstly, the size of the maximum inscribed circle in the dorsal region of the hand varies with the size of hand region, so there is no need to fix the radius of the inscribed circle, which can reduce the impact of artificially setting parameters.…”

Section: Image Preprocessingmentioning

confidence: 99%

Towards application of dorsal hand vein recognition under uncontrolled environment based on biometric graph matching

2018

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Dorsal hand vein recognition is a kind of biometric technique that has emerged in the last two decades. Owing to its safety, accuracy, and effectiveness, more and more researchers are involved in the study. Here, the authors presented a dorsal hand vein recognition system under uncontrolled environments based on biometric graph matching (BGM). Firstly, the authors establish two hand vein databases under natural indoor lighting conditions, i.e. XJTU-A and XJTU-B, with the hand not fixed. Secondly, the authors focus on optimising the image preprocessing steps in terms of region of interest (ROI) extraction, vein segmentation, and vein skeleton extraction. An 'open' operation with a large parameter is carried out to make the ROI extraction more abundant based on the maximum inscribed circle. In vein segmentation, the authors use the curvature point algorithm to better extract the vein skeleton. Thirdly, BGM algorithm is adopted to obtain distance measurements. The authors use single distance measure and multiple distance measures to obtain the threshold for recognition, respectively. Finally, the proposed dorsal hand vein recognition system is tested in three databases, and experiment results show that the improvement of the entire algorithms leads to high accuracy and strong robustness of the recognition system, whether under uncontrolled or controlled conditions.

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