Discriminative binary feature learning and quantization in biometric key generation

Highlights：

• 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.

• The facial features are extracted using a proposed equalized local binary pattern which showed promising results when simulated on standard face databases.

• To cater for variations and provide flexibility in error tolerance, we propose a quantization scheme which not only cater for the variations, it also aided in providing security and reducing the size of the features.

• A secure key generation mechanism is developed based on the facial features in which keys can be periodically updated.

• The robustness and security of the generated keys are evaluated on a set of standard statistical tests comprising of three requirements: randomness, weak biometric privacy and strong biometric privacy.