Personal authentication using partial palmprint and palmvein images with image quality measures

Nayar, Gayathri R.; Bhaskar, Anusree; Satheesh, Leshmi; Kumar, P. Santhosh; R.P, Aneesh

doi:10.1109/coconet.2015.7411186

Cited by 9 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[79] used dual-tree complex wavelet transform and opponentprocessing for fusion of palm vein and palmprint fusion. Nayar et al [80] composed palmprint and palm vein feature and use different feature extraction methods. Partial palmprint matching was accomplished by a recursive adjoint minutiae matching method where a coarse to fine matching technique was used.…”

Section: Palm Vein Fusion Recognitionmentioning

confidence: 99%

Review of palm vein recognition

Elliott

Lin

et al. 2019

IET biom.

View full text Add to dashboard Cite

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.

show abstract

Section: Palm Vein Fusion Recognitionmentioning

confidence: 99%

Review of palm vein recognition

Elliott

Lin

et al. 2019

IET biom.

View full text Add to dashboard Cite

show abstract

“…The vein pattern is unique from one human to another. In addition, the advantages of the use of palm veins as a biometric are that it is consistent [ 14 ] and the veins only exist in a human who is alive [ 15 , 16 ]; it is also accurate, contactless, cost-effective, convenient, and reliable [ 17 , 18 ]. The veins carry blood containing hemoglobin, which can be captured using an infrared camera with a wavelength spectrum of 750 nm–1 mm [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Extractor Using Discrete Wavelet Transform and Histogram of Oriented Gradient on Convolutional-Neural-Network-Based Palm Vein Recognition

Wulandari,

Chai,

Basari

et al. 2024

Sensors

View full text Add to dashboard Cite

Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER.

show abstract