Deep Face Fuzzy Vault: Implementation and Performance

Biometric technologies, especially face recognition, have become an essential part of identity management systems worldwide. In deployments of biometrics, secure storage of biometric information is necessary in order to protect the users' privacy. In this context, biometric cryptosystems are designed to meet key requirements of biometric information protection enabling a privacy-preserving storage and comparison of biometric data. This work investigates the application of a well-known biometric cryptosystem, i.e. the improved fuzzy vault scheme, to facial feature vectors extracted through deep convolutional neural networks. To this end, a feature transformation method is introduced which maps fixed-length real-valued deep feature vectors to integer-valued feature sets. As part of said feature transformation, a detailed analysis of different feature quantisation and binarisation techniques is conducted. 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. In experiments, an unlinkable improved deep face fuzzy vault-based template protection scheme is constructed employing features extracted with a state-of-the-art deep convolutional neural network trained with the additive angular margin loss (ArcFace). 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 an effective face-based fuzzy vault scheme providing privacy protection of facial reference data as well as digital key derivation from face.