Finger Vein Presentation Attack Detection Using Total Variation Decomposition

Qiu, Xinwei; Kang, Wenxiong; Tian, Senping; Jia, Wei; Huang, Zhixing

doi:10.1109/tifs.2017.2756598

Cited by 58 publications

(28 citation statements)

References 48 publications

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“…There are many open finger vein databases such as SDUMLA-HMT [13], HKPU-FV [14], UTFV [15], MMCBNU_6000 [16], THU-FV [17], FV-USM [18] University developed their finger vein database called (HKPU-FV) [14], which consists of finger vein and low texture images. In 2010, Shandong University released one multimodal trait database SDUMLA-FV [13].…”

Section: Methodsmentioning

confidence: 99%

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A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

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Section: Methodsmentioning

confidence: 99%

“…There are some Gabor filter-based FVR techniques. Kumar et al [14] used multi-orientation for finger vein pattern extraction. Yang et al proposed multi-channel Gabor filter and a bank of even-symmetric Gabor filter with eight orientations to get information about vein vessel [56,57].…”

Section: Vein-based Methodsmentioning

confidence: 99%

A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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“…The approach is compared to techniques from [252], including two LBP variants, and to quality-based approaches computing block-wise entropy, sharpness and standard deviation. Qiu et al [213] employ total variation regularisation to decompose original finger vein images into structure and noise components, which represent the degrees of blurriness and the noise distribution. Subsequently, a block local binary pattern descriptor is used to encode both structure and noise information in the decomposed components, the histograms of which are fed into an SVM classifier.…”

Section: Presentation Attack Detectionmentioning

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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“…In addition to the approaches presented in [71,75], [44], to finally conclude that the baseline LBP technique performs as good as its "improvements". Finally, in a combined approach, Qiu et al used total variation decomposition to divide the finger vein sample into its structural and noise components [58]. Using again LBP descriptors and SVMs, they achieved a perfect detection accuracy with APCER = BPCER = 0% over the VERA DB.…”

Section: Finger Vein Presentation Attack Detectionmentioning

confidence: 99%

“…Two years later, the first competition on finger vein PAD was organised [75], where three different teams participated. Since then, different PAD approaches have been presented, based on either a video sequence and motion magnification [60], texture analysis [44,61,71], image quality metrics [7], or more recently, neural networks [52,59,63] and image decomposition [58].…”

Section: Introductionmentioning

confidence: 99%

Presentation Attack Detection for Finger Recognition

Kolberg

Gomez‐Barrero

Venkatesh

et al. 2019

Advances in Computer Vision and Pattern Recognition

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Whereas other biometric characteristics, such as the face, are readily available for an eventual attacker through social media or easy to capture with a conventional smartphone, vein patterns can only be acquired with dedicated sensors. This fact makes them relevant not only for recognition purposes but especially for Presentation Attack Detection (PAD), for instance, in combination with fingerprint recognition. In this chapter, we make use of this combination and present a finger vein-based PAD algorithm to detect presentation attacks targeting fingerprint recognition. The experiments are carried out on a newly collected database, comprising 32 species of Presentation Attack Instruments ranging from printed artefacts to more sophisticated fingerprint overlays. The results show that our method preserves a convenient usage while detecting around 90% of the attacks. However, thin and transparent fingerprint overlays remain very challenging.

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Finger Vein Presentation Attack Detection Using Total Variation Decomposition

Cited by 58 publications

References 48 publications

A Systematic Review of Finger Vein Recognition Techniques

A Systematic Review of Finger Vein Recognition Techniques

State of the Art in Vascular Biometrics

Presentation Attack Detection for Finger Recognition

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