Multibiometric cryptosystem: model structure and performance analysis
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
A hybrid biometric cryptosystem for securing fingerprint minutiae templates
Pattern Recognition Letters
Multi-factor biometrics for authentication: a false sense of security
Proceedings of the 12th ACM workshop on Multimedia and security
Proceedings of the CUBE International Information Technology Conference
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Multibiometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in unibiometric systems. In this thesis, we address two critical issues in the design of a multibiometric system, namely, fusion methodology and template security. First, we propose a fusion methodology based on the Neyman-Pearson theorem for combination of match scores provided by multiple biometric matchers. The likeli-hood ratio (LR) test used in the Neyman-Pearson theorem directly maximizes the genuine accept rate (GAR) at any desired false accept rate (FAR). The densities of genuine and impostor match scores needed for the LR test are estimated using finite Gaussian mixture models. We also extend the likelihood ratio based fusion scheme to incorporate the quality of the biometric samples. Further, we also show that the LR framework can be used for designing sequential multibiometric systems by constructing a binary decision tree classifier based on the marginal likelihood ratios of the individual matchers. The LR framework achieves consistently high recognition rates across three different multibiometric databases without the need for any parameter tuning. For instance, on the WVU-Multimodal database, the GAR of the LR fusion rule is 85.3% at a FAR of 0.001%, which is significantly higher than the corresponding GAR of 66.7% provided by the best single modality (iris). The use of image quality information further improves the GAR to 90% at a FAR of 0.001%. Next, we show that the proposed likelihood ratio based fusion framework is also applicable to a multibiometric system operating in the identification mode. We further investigate rank level fusion strategies and propose a hybrid scheme that utilizes both ranks and scores to perform fusion in the identification scenario. While fusion of multiple biometric sources significantly improves the recognition accuracy, it requires storage of multiple templates for the same user corresponding to the individual biometric sources. Template security is an important issue in biometric systems because unlike passwords, stolen biometric templates cannot be revoked. Hence, we propose a scheme for securing multibiometric templates as a single entity using the fuzzy vault framework. We have developed fully automatic implementations of a fingerprint-based fuzzy vault that secures minutiae templates and an iris cryptosystem that secures iriscode templates. We also demonstrate that a multibiometric vault achieves better recognition performance and higher security compared to a unibiometric vault. For example, our multibiometric vault implementation based on fingerprint and iris achieves a GAR of 98.2% at a FAR of less than 0.01% and provides approximately 49 bits of security. The corresponding GAR values of the individual iris and fingerprint vaults are 88% and 78.8%, respectively. When the iris and fingerprint vaults are stored separately, the security of the system is only 41 bits.