An Integration of New Digital Image Scrambling Technique on PCA-Based Face Recognition System

Abstract

Systems using biometric authentication offer greater security than traditional textual and graphical password-based systems for granting access to information systems. Although biometric-based authentication has its benefits, it can be vulnerable to spoofing attacks. Those vulnerabilities are inherent to any biometric-based subsystem, including face recognition systems. The problem of spoofing attacks on face recognition systems is addressed here by integrating a newly developed image encryption model onto the principal component pipeline. A new model of image encryption is based on a cellular automaton and Gray Code. By encrypting the entire ORL faces dataset, the image encryption model is integrated into the face recognition system’s authentication pipeline. In order for the system to grant authenticity, input face images must be encrypted with the correct key before being classified, since the entire feature database is encrypted with the same key. The face recognition model correctly identified test encrypted faces from an encrypted features database with 92.5% accuracy. A sample of randomly chosen samples from the ORL dataset was used to test the encryption performance. Results showed that encryption and the original ORL faces have different histograms and weak correlations. On the tested encrypted ORL face images, NPCR values exceeded 99%, MAE minimum scores were over (>40), and GDD values exceeded (0.92). Key space is determined by $u\left(2^{size\left(A_0\right)}\right)$ where A0 represents the original scrambling lattice size, and u is determined by the variables on the encryption key. In addition, a NPCR test was performed between images encrypted with slightly different keys to test key sensitivity. The values of the NPCR were all above 96% in all cases.