Cancellable Fingerprint Biometrics for Secure Cryptographic Key Derivation Using CNN

The combination of biometric characteristics with cryptographic systems has become a strong solution to counter identity verification and data security challenges. This paper suggest a method of generating cryptographic keys from cancellable biometrics, with emphasis on fingerprint information. Through a four-stage approach—fingerprint preprocessing, feature extraction using Convolutional Neural Networks (CNNs), cancellable transformation, and cryptographic key generation this work proves a robust pipeline that maintains security as well as privacy. Experimental results confirm the effectiveness of the technique, opening doors to the advancement of secure biometric cryptosystems. The suggested architecture enhances security by enabling revocable biometric configurations, relieving the threats of format split the difference. The combination of PBKDF2 with SHA-256 ensures cryptographic key robustness by preventing beast power and table attacks. This research is highlighted by the ability of cancellable biometrics in real applications, providing an adaptable and security protecting verification component. Test outcomes confirm its effectiveness, and thus it is a promising solution to secure biometric-based cryptographic systems.