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Sensors 2015, 15(6), 14615-14638; doi:10.3390/s150614615

Fingerprint Liveness Detection in the Presence of Capable Intruders

INESC TEC—INESC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, Porto 4200-465, Portugal
Departamento de Engenharia Eletrotécnica e de Computadores, Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto 4200-465, Portugal
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 19 February 2015 / Revised: 19 May 2015 / Accepted: 25 May 2015 / Published: 19 June 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2458 KB, uploaded 23 June 2015]   |  


Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system. View Full-Text
Keywords: biometrics; liveness detection; fingerprint; supervised classification; semi-supervised classification biometrics; liveness detection; fingerprint; supervised classification; semi-supervised classification

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sequeira, A.F.; Cardoso, J.S. Fingerprint Liveness Detection in the Presence of Capable Intruders. Sensors 2015, 15, 14615-14638.

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