A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching

Liu, Fu; Jiang, Shoukun; Kang, Bing; Hou, Tao

doi:10.1109/access.2020.2988714

Cited by 14 publications

(11 citation statements)

References 36 publications

(51 reference statements)

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“…To evaluate and implement the proposed method, we implemented it on two datasets. First, the results were analyzed on the Jilin University -dorsal hand vein database [74], and in the next section, the results are shown on the 11K hands dataset [105]. It can be diagnosed by similar methods, such as (LBP) [80], local phase quantization (LPQ) [83], Gabor [83], scale-invariant feature transform (SIFT) [86], flamelt-generated manifolds (FGM) [107] and biometric graph matching (BGM) [46].…”

Section: Resultsmentioning

confidence: 99%

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Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

Alashik

Yıldırım

2021

IEEE Access

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In this research, biometric authentication, which has been widely used for different purposes in the last quarter-century, is studied. Dorsal hand veins are used for biometric authentication. "Deep learning" (DL) and "generative adversarial networks" (GANs) are used together as keys in the study. A DL-GAN is obtained by combining deep learning and GAN. The developed DL-GAN method is tested on two separate databases. The adversarial network (DL-GAN) method is developed to increase the authentication process's proportional value. For identity verification, dorsal hand veins with biometric physical properties are used. A multistep approach is used for selecting hand dorsal features, including preimage processing and effectively identifying individuals. The deep learning productive antinetwork method is used to effectively identify individuals based on the information obtained from the dorsal hand vein images. For the test in the study, two open access databases are used. These databases are the Jilin University -dorsal hand vein database and the 11K hands database. The results of the experiments performed on the dataset related to the dorsal hand vessels show that the DL-GAN method reaches an identity accuracy level of 98.36% and has an error rate of 2.47% and a standard accuracy of 0.19%. The accuracy of the experimental results in the second dataset is 96.43%, the equal error rate is 3.55% and the standard accuracy is 0.21%. The improved DL-GAN method obtains better results than physical biometric methods such as LBP, LPQ, GABOR, FGM, BGM and SIFT.

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Section: Resultsmentioning

confidence: 99%

“…Two different databases are used to evaluate the methods proposed in this paper, the Jilin University -dorsal hand vein database [74] and the 11K Hands dataset [105]. The Jilin University -dorsal hand vein database contains several real images and many artificial images for the analysis and evaluation of biometric methods.…”

Section: A Datasetmentioning

confidence: 99%

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

Alashik

Yıldırım

2021

IEEE Access

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“…Existing system of recognition also addressed the problems associated with partial occlusion. The study of Liu et al [16] has used a graph matching mechanism for addressing this problem of recognition system of dorsal hand. The authors have used conventional shape-based feature extraction mechanism for recognition of vein along with edge attributes.…”

Section: Related Studiesmentioning

confidence: 99%

A Novel Feature Extraction for Complementing Authentication in Hand-based Biometric

Mahalakshmi¹,

Sheela²

2021

IJACSA

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With an increasing usage of hand-based biometrics in authentication system, there is a need to evolve up with more potential security owing to increasing evolution of threats. The security of the hand authentication system completely depends upon uniqueness and distinct selection of features from hand image which has the properties of robustness, fault tolerance, and simpler implication. Review of existing feature extraction literatures shows more inclination towards sophisticated process as well as it also suffers from various other limitation. Therefore, this manuscript resolves this limitation by presenting a novel model of feature extraction which is carried out in more progressive form and less iterative form, unlike existing approaches. The proposed system achieves its research goal by introducing simplified feature extraction operation via storage, blurring, color space conversion, binary image conversion, the modelling aspect of the study emphasizes on image enhancement along with fuzzification for yielding more efficient result. An experimental study has been carried out using Python considering hand-biometric dataset in order to carry analysis where the outcome shows significant supportability over any palmprint recognition system. The study outcome is compared with most standard implementation of feature extraction to find that proposed system offer better accuracy performance in contrast with existing system.

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“…2 displays samples from the normal-blur database (the normal hand vein and normal-blur databases are freely available at https://github.com/JLUqiankun/Vein). Here, we extract circular region of interest (ROI) using maximum inscribed circle method outlined in [11], and the circular ROIs are normalised to 256 × 256 pixels. Fig.…”

Section: Image Acquisition and Databasesmentioning

confidence: 99%

“…Here, we extract circular region of interest (ROI) using maximum inscribed circle method outlined in [11 ], and the circular ROIs are normalised to 256 × 256 pixels. Fig.…”

Section: Image Acquisition and Databasesmentioning

confidence: 99%

Improved multiscale local phase quantisation histogram for the recognition of blurred dorsal hand vein images

et al. 2020

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Vein texture elements in images are sometimes degraded due to centrally symmetric blur, e.g. due to motion of hand or camera not being in focus, which significantly affects recognition systems performance. Multiscale local phase quantisation (MLPQ) can effectively improve system accuracy in the presence of blur by dividing vein images into non-overlapping blocks and extracting multiscale local phase information. However, MLPQ misses some important discriminant information at the intersections of different regions, which leaves room for improving the system performance. In the present work, a multiscale overlapping blocks local phase quantisation (MOLPQ) histogram algorithm is presented to divide the vein image into overlapping blocks and extract multiscale local phase information, which includes the discriminant information lost in MLPQ. MOLPQ is validated via comparison with state-of-the-art recognition algorithms on a normal hand vein database, artificial-blur databases, and a normal-blur database, and the accuracies on the normal hand vein and normal-blur databases are 99.57 and 98.07%, respectively. Additionally, MOLPQ outperforms other methods on all databases in terms of accuracy.

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A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching

Cited by 14 publications

References 36 publications

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

A Novel Feature Extraction for Complementing Authentication in Hand-based Biometric

Improved multiscale local phase quantisation histogram for the recognition of blurred dorsal hand vein images

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