Fingerprint Liveness Detection Based on Multiple Image Quality Features

Jin, Chi; Li, Shengzhe; Kim, Hakil; Park, Ensoo

doi:10.1007/978-3-642-17955-6_21

Cited by 10 publications

(9 citation statements)

References 11 publications

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“…The Tan and Schuckers [30] fingerprints are compared with the fingerprints obtained from genuine living fingers. Jin et al [31] suggest that the middle ridge and the middle valley signals are interesting features that can be used to distinguish between living and fake fingers. They skeletonise the fingerprint and its inverted version in order to obtain the skeletons of the ridge and the valley structures.…”

Section: Static Methodsmentioning

confidence: 99%

“…Even though this phenomenon of perspiration can be used in the dynamic methods by observing changes in the scan over time, there are also suggestions that this phenomenon is important even in the case of a single 2D scan of a fingerprint. Jin et al [31] observed that even in a single 2D scan, the pores obtained by using a living finger look different than the pores obtained by using a fake finger because of the perspiration phenomenon. The perspiration phenomenon is utilised by Tan and Schuckers in [33].…”

Section: Static Methodsmentioning

confidence: 99%

“…Jin et al . [31] observed that even in a single 2D scan, the pores obtained by using a living finger look different than the pores obtained by using a fake finger because of the perspiration phenomenon. The perspiration phenomenon is utilised by Tan and Schuckers in [33].…”

Section: Pad For Fingerprint Sensorsmentioning

confidence: 99%

See 2 more Smart Citations

Presentation attack detection methods for fingerprint recognition systems: a survey

Sousedik¹,

Busch²

2014

IET biom.

182

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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Section: Static Methodsmentioning

confidence: 99%

Section: Static Methodsmentioning

confidence: 99%

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Presentation attack detection methods for fingerprint recognition systems: a survey

Sousedik¹,

Busch²

2014

IET biom.

182

107

View full text Add to dashboard Cite

show abstract

“…Furthermore, a local clarity score is computed based on the overlap area between the gray-level distributions of ridges and valley, which has to be very small for high-clarity ridges/valleys. Quality measures that have been effectively used for spoof detection are spectral band energy, middle ridge line, and middle valley line [Jin et al 2011]. Due to the difficulty of copying pores along the ridges during the spoof creation process, the middle ridge line signals of spoof have fewer periodic peaks compared to live fingerprints.…”

Section: Quality Basedmentioning

confidence: 99%

A Survey on Antispoofing Schemes for Fingerprint Recognition Systems

2014

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Several issues related to the vulnerability of fingerprint recognition systems to attacks have been highlighted in the biometrics literature. One such vulnerability involves the use of artificial fingers, where materials such as Play-Doh, silicone, and gelatin are inscribed with fingerprint ridges. Researchers have demonstrated that some commercial fingerprint recognition systems can be deceived when these artificial fingers are placed on the sensor; that is, the system successfully processes the ensuing fingerprint images, thereby allowing an adversary to spoof the fingerprints of another individual. However, at the same time, several countermeasures that discriminate between live fingerprints and spoof artifacts have been proposed. While some of these antispoofing schemes are hardware based, several software-based approaches have been proposed as well. In this article, we review the literature and present the state of the art in fingerprint antispoofing. ACM Reference Format:Emanuela Marasco and Arun Ross. 2014. A survey on antispoofing schemes for fingerprint recognition systems.

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“…The standard deviation of the noise residue was used as the distinguishable feature. Jin et al [14] utilized three effective quality measures, namely spectral band energy, middle ridge line and middle valley line, to extract features. The support vector machine and quadratic discriminant analysis classifiers were trained.…”

Section: Related Workmentioning

confidence: 99%