Efficient private key generation from iris data for privacy and security applications.

Private key generation based on biometric data is gaining popularity in several cryptographic applications. Some key generation approaches using fingerprint and face data are known for this purpose. The existing approaches, in general, follow direct features from raw data, which may have an issue of revealing users' biometric data. Further, iris biometrics being a better source for stable and secure key generation, is yet to be explored. In this work, an iris biometric-based key generation approach is proposed. In the proposed method, first, an ensemble L0 Gradient Minimization and an edge-preserving filter are used to extract iris structure from the iris images. Secondly, the iris texture data is normalized, and the consistent region is selected based on a novel statistical method. Thirdly, feature vectors are generated using the ensemble local space-filling curve descriptors and their variants. Fourth, a discriminant feature vector is generated using hybrid feature selection and neighbourhood component analysis. Finally, an interval-based encoding scheme is used to generate a key from the discriminant feature vector. To substantiate the efficacy of the proposed approach, extensive experiments have been carried out with near-infrared images, visible wavelength images, at-a-distance, and on-move images. Further, distinctiveness, dissimilarity, randomness, and security analyses have been carried out to validate the resilience of keys against different attacks. More significantly, a unique key can be generated dynamically, that is, from an online image. The proposed approach is applicable to different cryptographic applications, such as data storage security, remote authentication protocol, blockchain system security, etc.