Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

Wang, Yiding; Duan, Qiangyu; Shark, Lik‐Kwan; Huang, Di

doi:10.1504/ijcat.2016.079870

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…2 ) were randomly chosen as the training set. A detailed comparison was conducted with the local binary pattern (LBP) [13 ], histogram of oriented gradient (HOG) [14 ], Weber local descriptor (WLD) [15 ], scale‐invariant feature transform (SIFT) [16, 17 ], Gabor [18 ], LPQ [8 ], and MLPQ [6 ], which are state‐of‐the‐art algorithms in dorsal hand vein recognition. The correlation coefficient between adjacent pixels ρ was 0.99; the sizes of local neighbours M were 23 × 23, 25 × 25, and 27 × 27, respectively; the values of the corresponding frequency parameter a were 1/23, 1/25, and 1/27, respectively.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

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

et al. 2020

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“…However, scars are skin markings that have a geometric distribution contributing to the identification of individuals [52]. The last parameter considered concerning local contrast is the hand position, which causes geometric and perspective variations due to inconsistent positions and postures of the hands with respect to the camera [53]. It is worth noting that [54] shows that tilting, bending, planar and nonplanar rotations cause severe performance degradations of publicly available datasets.…”

Section: Acquisition Of Palm Vein Imagesmentioning

confidence: 99%

Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review

Salazar-Jurado,

Hernández-García,

Vilches-Ponce

et al. 2022

Preprint

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With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein recognition has turned out to be challenging, mainly due to the noise and irregular variations included at the time of acquisition. Meanwhile, existing palm vein recognition datasets are usually collected under near-infrared light, lacking detailed annotations on attributes (e.g., pose), so the influences of different attributes on vein recognition have been poorly investigated. Therefore, this paper examines the suitability of synthetic vein images generated to compensate for the urgent lack of publicly available large-scale datasets. Firstly, we present an overview of recent research progress of palm vein recognition, from the basic background knowledge to vein anatomical structure, data acquisition, public database, and quality assessment procedures. Then, we focus on the state-of-the-art methods that have allowed the generation of vascular structures for biometric purposes and the modeling of biological networks with their respective application domains. In addition, we review the existing research on the generation of style transfer and biological nature-based synthetic palm vein images algorithms. Afterward, we formalize a general flowchart for the creation of a synthetic database comparing real palm vein images and generated synthetic samples to obtain some understanding into the development of the realistic vein imaging system. Ultimately, we conclude by

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“…In this method, they are reconstructed. identifier for individual identification [82]-, [83]. Wei used hyperspectral images of the dorsal hand vein to extract discriminative local features [84].…”

Section: Related Workmentioning

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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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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Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

Cited by 13 publications

References 21 publications

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

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

Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review

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

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