Multi-Biometric Fuzzy Vault based on Face and Fingerprints

The fuzzy vault scheme has been established as cryptographic primitive suitable for privacy-preserving biometric authentication. To improve accuracy and privacy protection, biometric information of multiple characteristics can be fused at feature level prior to locking it in a fuzzy vault. We construct a multi-biometric fuzzy vault based on face and multiple fingerprints. On a multi-biometric database constructed from the FRGCv2 face and the MCYT-100 fingerprint databases, a perfect recognition accuracy is achieved at a false accept security above 30 bits. Further, we provide a formalisation of feature-level fusion in multi-biometric fuzzy vaults, on the basis of which relevant security issues are elaborated. Said security issues, for which we define countermeasures, are commonly ignored and may impair the overall system's security.

Deep Face Fuzzy Vault: Implementation and Performance

Deep convolutional neural networks have achieved remarkable improvements in facial recognition performance. Similar kinds of developments, e.g. deconvolutional neural networks, have shown impressive results for reconstructing face images from their corresponding embeddings in the latent space. This poses a severe security risk which necessitates the protection of stored deep face embeddings in order to prevent from misuse, e.g. identity fraud. In this work, an unlinkable improved deep face fuzzy vault-based template protection scheme is presented. To this end, a feature transformation method is introduced which maps fixed-length real-valued deep face embeddings to integer-valued feature sets. As part of said feature transformation, a detailed analysis of different feature quantisation and binarisation techniques is conducted using features extracted with a state-of-the-art deep convolutional neural network trained with the additive angular margin loss (ArcFace). At key binding, obtained feature sets are locked in an unlinkable improved fuzzy vault. For key retrieval, the efficiency of different polynomial reconstruction techniques is investigated. The proposed feature transformation method and template protection scheme are agnostic of the biometric characteristic and, thus, can be applied to virtually any biometric features computed by a deep neural network. For the best configuration, a false non-match rate below 1% at a false match rate of 0.01%, is achieved in cross-database experiments on the FERET and FRGCv2 face databases. On average, a security level of up to approximately 28 bits is obtained. This work presents the first effective face-based fuzzy vault scheme providing privacy protection of facial reference data as well as digital key derivation from face.