Discriminative binary feature learning and quantization in biometric key generation

Biometric key generation based on facial features is far more complicated than face recognition, the problem being that there is no relevant information saved in databases for matching facial features in the key generation system, contrary to the face recognition system. In this paper, first, we develop an efficient unified framework for generating stable, robust and secure cryptography keys based on facial features, without the need to save information related to facial features in the database. Second, the facial features are extracted using a proposed equalized local binary pattern which shows promising results when simulated on standard face databases. Third, to cater for variations and provide flexibility in error tolerance, we propose a quantization scheme which not only cater for the variations, it also aids in providing security and reducing the size of the features. Fourth, a secure key generation mechanism is developed based on the facial features in which keys can be periodically updated. Fifth, the robustness and security of the generated keys are evaluated on a set of standard statistical tests comprising three requirements: randomness, weak biometric privacy and strong biometric privacy. Lastly, comparing our approach with several state-of-the-art methods, it exhibited superior performance.