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.
Highlights
Biometric recognition is nowadays a mature technology used in many government and civilian applications such as e-passports, ID cards, and border control
We start with the traditional approach using a binary classifier and training and testing within each dataset, we introduce modifications in the training and testing datasets and end up with a study where only the information about the live samples is used for training our models
Discussion of Results Obtained in Study 1 and from State-Of-The-Art Methods
Summary
Biometric recognition is nowadays a mature technology used in many government and civilian applications such as e-passports, ID cards, and border control. States Visitor and Immigrant Status Indicator Technology) fingerprint system, the Privium iris system at Schiphol airport, and the SmartGate face system at Sydney Airport [1]. Recognition systems based on fingerprints (FRS) in particular are widely used, probably this was the first biometric trait to be used to identify people. Fingerprints are small lines/ridges and valleys in the skin of fingertips. Their configuration is formed at around the seventh month of fetus development due to a combination of genes and environmental factors and do not change throughout life (except if an accident, such as a severe burn, happens) [2,3]. The influence of environmental factors in the fingerprint formation results in such variations in its configuration that it is considered impossible to have two fingerprint looking exactly alike [2,4]
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