Dynamic liveness and forgeries detection of the finger surface on the basis of spectroscopy in the 400–1650nm region

Hengfoss, Clarissa; Kulcke, Axel; Mull, Günther; Edler, Carolin; Püschel, Klaus; Jopp, Eilin

doi:10.1016/j.forsciint.2011.05.014

Cited by 16 publications

(17 citation statements)

References 15 publications

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“…To that end, some hardware-based approaches utilise different illumination techniques or capture the pulse frequencies. Hengfoss et al [34] analysed in 2011 the reflections for all wavelengths between 400 and 1650 nm on the blanching effect. This effect appears when the finger is pressed against a surface and the blood is squeezed out due to the compression of the tissue.…”

Section: Fingerprint Presentation Attack Detectionmentioning

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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Section: Fingerprint Presentation Attack Detectionmentioning

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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“…Periodical changes in the absorption of light from the two sources are analysed in order to identify whether a genuine living finger is being scanned. Hengfoss et al [61] have analysed the absorption of light of various wavelengths in the living and the fake/cadaver fingers by using a spectrometer. They have also analysed the periodic changes of the light absorption in time, because of the blood circulation.…”

Section: Hardware-based Liveness Detectionmentioning

confidence: 99%

“…Another possibility is to illuminate the finger by a multi-spectral light source, and analyse the whole transmission/reflectance signature in the range of the spectrum. Hengfoss et al [61] have published an extensive analysis of the multi-spectral signatures of the living against the fake/cadaver fingers. They emphasise another interesting phenomenon that is a possible means of classification.…”

Section: Hardware-based Liveness Detectionmentioning

confidence: 99%

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Presentation attack detection methods for fingerprint recognition systems: a survey

Sousedik¹,

Busch²

2014

IET biom.

186

107

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Nowadays, fingerprint biometrics is widely used in various applications, varying from forensic investigations and migration control to access control as regards security sensitive environments. Any biometric system is potentially vulnerable against a fake biometric characteristic, and spoofing of fingerprint systems is one of the most widely researched areas. The state-of-the-art sensors can often be spoofed by an accurate imitation of the ridge/valley structure of a fingerprint. An individual may also try to avoid identification by altering his own fingerprint pattern. This study is a survey of presentation attack detection methods for fingerprints, both in terms of liveness detection and alteration detection.

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“…Liveness detection can be achieved in many different ways, e.g., by checking vital signs such as variability of the image caused by blood flow [ 18 ]. Another option is to do a spectroscopy scan of the object that is being scanned and to check its similarity to the human tissue spectrogram [ 19 ]. Moreover, there are also methods that are focused on the conductivity of a tissue, but they require direct contact with a human body [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network

Stanuch

Wodziński

Skalski

2020

Sensors

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Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding supplementary modality while keeping the user experience at the same level. Palm vein systems are based on infrared wavelengths used for capturing images of users’ veins. It is both convenient for the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV wavelengths; the images are then processed by a deep convolutional neural network for extraction of biometric features and authentication of users. We tested the system in a verification scenario that consisted of checking if the images collected from the user contained the same biometric features as those in the database. The True Positive Rate (TPR) achieved by the system when the information from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal Error Rate (EER).

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Dynamic liveness and forgeries detection of the finger surface on the basis of spectroscopy in the 400–1650nm region

Cited by 16 publications

References 15 publications

Presentation Attack Detection for Finger Recognition

Presentation Attack Detection for Finger Recognition

Presentation attack detection methods for fingerprint recognition systems: a survey

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network

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