A Hybrid CNN-SVR Framework for Robust and Privacy-Preserving Cancelable Fingerprint Recognition

Abstract

Biometric authentication is commonly adopted because biometric traits are unique and difficult to copy. Fingerprint recognition, in particular, is widely used in many practical systems, but privacy remains a serious concern. Many existing fingerprint systems store biometric templates in a form that can be misused if leaked. In these situations, attackers may reconstruct or reverse the data, revealing sensitive personal information. Since biometric characteristics cannot be replaced like passwords, any compromise may have long-term consequences. For this reason, privacy-preserving biometric designs are increasingly necessary. This study presents a cancelable fingerprint recognition system that combines Convolutional Neural Networks (CNNs) and Support Vector Regression (SVR). The CNN part of the system is utilized to learn the distinctive features of the fingerprints from the images directly, without the need to define them through manual engineering. The features obtained have a good degree of robustness to typical changes such as rotation, noise, and variability in the acquisition process. These features are further processed through an SVR model, unlike being stored in the form of fingerprint templates, thus preventing the risk of reconstruction attacks. Evaluation was performed on fingerprint images from the FVC2004 database, using image augmentation to simulate variability in pressure, orientation, and illumination conditions. The proposed framework achieved a recognition accuracy of 99.4%, an Equal Error Rate (EER) of 1.0%, and an Area Under the Curve (AUC) of 0.994, demonstrating strong robustness and reliability for privacy-preserving cancelable fingerprint recognition.