Finger-vein image matching based on adaptive curve transformation

Yang, Jinfeng; Shi, Yihua; Jia, Guorui

doi:10.1016/j.patcog.2017.01.008

Cited by 91 publications

(33 citation statements)

References 19 publications

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“…A different approach not explicitly leading to a binary vascular network as template is the employment of a set of Spatial Curve Filters (SCFs) with variations in curvature and orientation [292]. Thus the vascular network consists of vessel curve segments.…”

Section: Finger Vein Recognition Toolchainmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.

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Section: Finger Vein Recognition Toolchainmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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“…That is to say, to obtain vein patterns in various different directions from the full image, the profiles of all orientations were analyzed in order to extract vein areas by asserting that areas with higher vein center position scores were more likely to be veins [ 13 ]. Yang et al proposed a method to extract the vein vector fields (VVF) of finger-veins based on spatial curve filters (SCF), Gaussian weight models, and curve-length fields (CLF), and conducted the matching based on phase-only correlation (POC) [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor

Kim

Song

Park

2018

Sensors

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Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods.

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“….Typical framework for finger vein recognition [9] Finger vein comes under the category of intrinsic features, and is therefore hard to replicate and alter. In a trans-illumination manner, near-infrared (NIR) light (700-900 nm) is used to capture finger veins [10]. For personal verification, systems based on veins generally use diverse anatomic features like finger vein, hand vein, foot vein or palm vein.…”

Section: Introductionmentioning

confidence: 99%

Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization

Jumaa¹,

Zidan²

2019

PEN

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This paper evaluates a set of enhancement stages for finger vein enhancement that not only has low computational complexity but also high distinguishing power. This proposed set of enhancement stages is centered around fuzzy histogram equalization. Two sets of evaluation are carried out: one with the proposed approach and one with another unique approach that was formulated by rearranging and cropping down the preprocessing steps of the original proposed approach. To extract features, a combination of Hierarchical Centroid and Histogram of Gradients was used. Both enhancement stages were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results showed improvement as compared to three latest benchmarks in this field that used 6-fold validation.

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Finger-vein image matching based on adaptive curve transformation

Cited by 91 publications

References 19 publications

State of the Art in Vascular Biometrics

State of the Art in Vascular Biometrics

Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor

Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization

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