Secure cancelable face recognition system based on inverse filter

Biometric systems have recently become popular for modern security applications. Unfortunately, these systems have been the target of many hacking attempts. Biometrics in biometric databases will be lost forever if they are hacked and stolen. As a result, there is an urgent need to implement modern upgradable biometric systems. Cancelable biometrics is based on the principle of transforming biometric data to alternate templates that cannot be easily exploited by an impostor or attacker and can be discarded if violated. The idea in this paper depends on the inverse filter in a cancelable face recognition system. Masked biometric images are produced in the proposed cancelable face recognition system by blurring, noise addition, and then inverse filtering. In image restoration theory, it is well understood that inverse filtering contributes to noise enhancement, which is an undesirable effect. On the contrary, this result would be desired in cancelable biometric systems. When noise is magnified, it can obscure the original biometrics, resulting in cancelable templates. This is the philosophy that underpins the proposed system. The proposed system was tested on the Olivetti and Oracle (ORL) dataset, the Labeled Faces in the Wild (LFW) database, and the Face Recognition Technology (FERET) database. Simulation results using evaluation metrics such as non-invertibility, unlinkability, visual inspection, false positive rate, false negative rate, Equal Error Rate (EER), decidability, correlation coefficient, and Area under the Receiver Operating Characteristic curve (AROC) show that the proposed cancelable biometric recognition system is quite effective for several security applications.