Power spectrum-based fingerprint vitality detection

Coli, Pietro; Marcialis, Gian Luca; Roli, Fabio

doi:10.1109/autoid.2007.380614

Cited by 63 publications

(43 citation statements)

References 5 publications

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“…Information such as: perspiration, ridge frequencies, skin elasticity, power spectrum of a fingerprint image, textural characteristics, etc. can be obtained by using software processing techniques [4,5,[15][16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Curvelet-based fingerprint anti-spoofing

Nikam

Agarwal

2009

SIViP

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Existing perspiration-based liveness detection algorithms need two successive images (captured in certain time interval), hence they are slow and not useful for real-time applications. Liveness detection methods using extra hardware increase the cost of the system. To alleviate these problems, we propose new curvelet-based method which needs only one fingerprint to detect liveness. Wavelets are very effective in representing objects with isolated point singularities, but fail to represent line and curve singularities. Curvelet transform allows representing singularities along curves in a more efficient way than the wavelets. Ridges oriented in different directions in a fingerprint image are curved; hence curvelets are very significant to characterize fingerprint texture. Textural measures based on curvelet energy and co-occurrence signatures are used to characterize fingerprint image. Dimensionalities of feature sets are reduced by running Pudil's sequential forward floating selection (SFFS) algorithm. Curvelet energy and co-occurrence signatures are independently tested on three different classifiers: AdaBoost.M1, support vector machine and alternating decision tree. Finally, all the aforementioned classifiers are fused using the "Majority Voting Rule" to form an ensemble classifier. A fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created by using varieties of artificial materials for casts and moulds of spoof fingerprints. Performance of the new liveness detection approach is found very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

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Section: Related Workmentioning

confidence: 99%

Curvelet-based fingerprint anti-spoofing

Nikam

Agarwal

2009

SIViP

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“…cluster shade and cluster prominence of the co-occurrence matrix. Coli et al [19] claimed that the high frequency details of the spoof fingerprint images were greatly reduced, and extracted features from the power spectrum for the classification. Nikam and Agarwal proposed several liveness detection methods based on the texture analysis of the fingerprint images.…”

Section: Related Workmentioning

confidence: 99%

Spoof Fingerprint Detection based on Co-occurrence Matrix

Jiang¹,

Liu²

2015

IJSIP

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“…Other liveness detection approaches for fake fingerprint detection include the analysis of perspiration and elasticity related features in fingerprint image sequences [42], the use of electric properties of the skin [43], using wavelets for the analysis of the finger tip surface texture [44], the use of the power spectrum of the fingerprint image [45], or analyzing the ring patterns of the Fourier spectrum [46].…”

Section: Related Workmentioning

confidence: 99%

A high performance fingerprint liveness detection method based on quality related features

Galbally

Alonso‐Fernandez

Fiérrez-Aguilar

et al. 2012

Future Generation Computer Systems

181

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tA new software-based liveness detection approach using a novel fingerprint parameterization based on quality related features is proposed. The system is tested on a highly challenging database comprising over 10,500 real and fake images acquired with five sensors of different technologies and covering a wide range of direct attack scenarios in terms of materials and procedures followed to generate the gummy fingers. The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake. This last characteristic provides the method with very valuable features as it makes it less intrusive, more user friendly, faster and reduces its implementation costs.

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Power spectrum-based fingerprint vitality detection

Cited by 63 publications

References 5 publications

Curvelet-based fingerprint anti-spoofing

Curvelet-based fingerprint anti-spoofing

Spoof Fingerprint Detection based on Co-occurrence Matrix

A high performance fingerprint liveness detection method based on quality related features

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