Empirical Evaluation of LBP-Extension Features for Finger Vein Spoofing Detection

Kocher, Daniel; Schwarz, Štefan; Uhl, Andreas

doi:10.1109/biosig.2016.7736921

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…As for other biometric characteristics, texture patterns have been extensively analysed for finger vein PAD. 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].…”

Section: Finger Vein Presentation Attack Detectionmentioning

confidence: 94%

“…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%

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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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Section: Finger Vein Presentation Attack Detectionmentioning

confidence: 94%

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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“…Texture-based PAD techniques have been proven to be applicable to the imagery in the FV-Spoofing-Attack database [253] independent of the above-referenced competition, in particular, baseline LBP [220]. Inspired by the success of basic LBP techniques [181,253] in finger vein PAD and the availability of a wide variety of LBP extensions and generalisations in the literature, [123] has empirically evaluated different features obtained by using these more recent LBP-related feature extraction techniques for finger vein spoofing detection. Additionally, the steerable pyramid is used to extract features subsequently used for FV spoofing detection [220].…”

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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“…However, it is still possible to spoof the finger-vein recognition system by using a stolen finger-vein image. As proven in previous research [ 23 , 24 , 25 , 26 , 27 , 28 ], the spoofing-attack can be done by printing the stolen finger-vein image on certain materials (such as paper or film) using carbon ink and attaching it on a real (live) finger during the image acquisition. Therefore, spoof detection methods, named as presentation attack detection (PAD) methods, for finger-vein biometric systems are necessary to protect the finger-vein recognition system from spoofing attacks.…”

Section: Introductionmentioning

confidence: 99%

“…Over time, many researchers have proposed various methods for PAD for finger-vein recognition system [ 23 , 24 , 25 , 26 , 27 , 28 ]. One of the earliest studies conducted by Qin et al [ 28 ] used the dynamic information from successive images to detect the real finger-vein images.…”

Section: Introductionmentioning

confidence: 99%

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

Nguyen

Yoon

Pham

et al. 2017

Sensors

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Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.

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Empirical Evaluation of LBP-Extension Features for Finger Vein Spoofing Detection

Cited by 14 publications

References 6 publications

Presentation Attack Detection for Finger Recognition

Presentation Attack Detection for Finger Recognition

State of the Art in Vascular Biometrics

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

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