MOBAA-OBCM: Multiobjective Biometric Authentication Approach and Optimized Beta Chaotic Map

Abstract

Applications for information security are increasingly using biometrics authentication. Multi-factor Authentication (MFA) is an authentication technique which needs the user to provide two or additional authentication factors to access a device which increases the security of biometrics systems. This study proposes an effective image encryption method based on Differential Evolution (DE), Optical Processing, and Optimized Beta Chaotic Map (OBCM). It is proposed to combine optical processing and OBCM by biometrics to create the Multiobjective Biometric Authentication Approach (MOBAA), which offers a more complex approach. A user password serves as the seed of a beta chaotic map, representing a knowledge factor, while an interferogram produced by an optical authentication approach serves as a possession factor. Multiobjective fitness function is used to determine the ideal beta chaotic map parameter in DE algorithm. The effectiveness of the proposed method is evaluated against recently created, other image encryption methods. On both grayscale and colour images, the proposed technique's performance has been assessed. The Number of Pixel Change Rates (NPCR), Unified Average Change Intensity (UACI), Peak Signal to Noise Ratio (PSNR), and Mean Absolute Error (MAE) results of the authentication methods are compared with those of existing methods.