Secure Multibiometric Systems

Although the market for biometric technologies is expanding, the existing biometric systems present still some issues that the research community has to address. In particular, in adverse environmental conditions (e.g., low quality biometric signals), where the error rates increase, it is necessary to create more robust and dependable systems. In the literature on biometrics, the integration of multiple biometric sources has been successfully used to improve the recognition accuracy of the unimodal biometric systems. Multibiometric systems, by exploiting more information, such as different biometric traits, multiple samples, multiple algorithms, make more reliable the biometric authentication. Benefits of multibiometrics depend on the diversity among the component matchers and also, on the competence of each one of them. In non-controlled conditions of data acquisition, there is a degradation of biometric signal quality that often causes a significant deterioration of recognition performance. It is intuitive the concept that, the classifier having the higher quality is more credible than a classifier operating on noisy data. Then, researchers started to propose quality-based fusion schemes, where the quality measures of the samples have been incorporated in the fusion to improve performance. Another promising direction in multibiometrics is to estimate the decision reliability of the component modality matcher based on the matcher output itself. An interesting open research issue concerns how to estimate the decision reliability and how to exploit this information in a fusion scheme. From a security perspective, a multimodal system appears more protected than its unimodal components, since spoofing two or more modalities is harder than spoofing only one. However, since a multimodal system involves different biometric traits, it offers a higher number of vulnerable points that may be attacked by a hacker who may choice to fake only a subset of them. Recently, researchers investigated if a multimodal system can be deceived by spoofing only a subset but not all the fused modalities. The goal of this thesis is to improve the performance of the existing integration mechanisms in presence of degraded data and their security in presence of spoof attacks. Our contribution concerns three important issues: 1) Reducing verification errors of a fusion scheme at score level based on the statistical Likelihood Ratio test, by adopting a sequential test and, when the number of training samples is limited, a voting strategy. 2) Addressing the problem of identification errors, by setting up a predictor of errors. The proposed predictor exploits ranks and scores generated by the identification operation and can be effectively applied in a multimodal scenario. 3) Improving the security of the existing multibiometric systems against spoof attacks which involve some but not all the fused modalities. Firstly, we showed that in such a real scenario performance of the system dramatically decrease. Then, for the fingerprint modality, we proposed a novel liveness detection algorithm which combines perspirationand morphology-based static features. Finally, we demonstrated that, by incorporating our algorithm in the fusion scheme, the multimodal system results robust in presence of spoof attacks.