Limits...
Fingerprint Liveness Detection in the Presence of Capable Intruders.

Sequeira AF, Cardoso JS - Sensors (Basel) (2015)

Bottom Line: 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 Article: PubMed Central - PubMed

Affiliation: INESC TEC-INESC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, Porto 4200-465, Portugal. afps@inesctec.pt.

ABSTRACT
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.

No MeSH data available.


Fingerprint recognition system block diagram.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4507655&req=5

f1-sensors-15-14615: Fingerprint recognition system block diagram.

Mentions: Most of the fingerprint recognition and classification algorithms perform some preprocessing, segmentation and enhancement steps to simplify the task of minutiae extraction. The main steps of a FRS system may be seen in Figure 1. It is worth highlighting the estimation of the foreground mask which is a crucial step to all the following stages of the process.


Fingerprint Liveness Detection in the Presence of Capable Intruders.

Sequeira AF, Cardoso JS - Sensors (Basel) (2015)

Fingerprint recognition system block diagram.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507655&req=5

f1-sensors-15-14615: Fingerprint recognition system block diagram.
Mentions: Most of the fingerprint recognition and classification algorithms perform some preprocessing, segmentation and enhancement steps to simplify the task of minutiae extraction. The main steps of a FRS system may be seen in Figure 1. It is worth highlighting the estimation of the foreground mask which is a crucial step to all the following stages of the process.

Bottom Line: 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 Article: PubMed Central - PubMed

Affiliation: INESC TEC-INESC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, Porto 4200-465, Portugal. afps@inesctec.pt.

ABSTRACT
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.

No MeSH data available.