Low‐cost biometric recognition system based on NIR palm vein image

Wu, Wei; Elliott, Stephen J.; Lin, Sen; Yuan, Weiqi

doi:10.1049/iet-bmt.2018.5027

Cited by 25 publications

(20 citation statements)

References 33 publications

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“…Apart from hand properties, there are other important biometric properties. One of them is dorsal hand veins [27][28][29] and palm veins [30][31], and hand vein biometry and authentication are also used in many parts of daily life. The main areas of use are airports, banking systems and everywhere where security is high.…”

Section: Hand Biometricmentioning

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: Hand Biometricmentioning

confidence: 99%

“…The G network first produces random images. Network D examines the images and tells G how similar the images are to the actual image [30].…”

Section: Generative Adversarial Network (Gan)mentioning

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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“…Wu et al . [34] found wrist side maximal inscribed circle (WSMIC) inside palm contour first. The WSMIC was the maximum inscribed circle inside the palm contour which falls on the side close to the wrist.…”

Section: Preprocessingmentioning

confidence: 99%

Review of palm vein recognition

Elliott

Lin

et al. 2019

IET biom.

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As a new research focus in biometrics, palm vein recognition has attracted people's attention because of high security, liveness-detection, user acceptability and convenience. Palm vein recognition exhibits high security, as it uses the network of blood vessels underneath palm skin for recognition. As palm vein is interior biological information of the body, vein patterns are much harder for intruders to copy compared to other biometric features. Palm vein is mostly invisible to the human eyes; they are commonly captured under near-infrared light. Under its natural state, the veins of the palm are concealed naturally and, for most of time people's hands are in the gesture of half fist. This study presents a comprehensive overview of recent research progress of palm vein recognition, from the basic background knowledge to data acquisition, public database, preprocessing, feature extraction and matching. In addition, the study focuses on palm vein-related fusion. Ultimately, the authors discuss the challenges and future perspectives in palm vein recognition for further works.

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“…Palmprint recognition frameworks are typically composed of four steps, which are acquisition, preprocessing, feature extraction and template storage (in enrollment)/template matching (in verification). The palmprint images of different types are acquired with various sensors, including 2D, 3D, vein, high resolution and multispectral palmprint images [2–4]. In preprocessing, the main objectives are enhancing images and localising the region of interest (ROI) [5–7].…”

Section: Introductionmentioning

confidence: 99%