A KCP-DCNN Multimodal Biosensor Authentication Device with Two-Step Verification and QR Code Falsification

Multi-biometric authentication systems have become a viable way to improve authentication performance in the current digital era.Several multi-biometric authentication studies have been carried out and published in the literature.The difficulties of separating real biometric information from fraudulent attempts and integrating biometric and non-biometric authentication methods in a "Deep Convolutional Neural Network (KCP-DCNN)" that makes use of Kernel Correlation Padding are highlighted in this paper.An efficient multimodal Biometric Authentication (BA) system that integrates fingerprint, signature, and face modalities is presented in the study.To get ready for picture improvement, the input images are first pre-processed using the "Radial Basis Function-centric Pixel Replication Technique (RBF-PRT)".This procedure uses" Log Z-Score-centric Generative Adversarial Networks (LZS-GAN)" to apply blurring, augmentation, and noise reduction techniques to improve the visual quality of photographs.Following this, Dlib's 68-point facial landmark extraction is performed using the enlarged signature, fingerprint, and enhanced face photos.Using a generative adversarial network (GAN) that generates new images using log Z-scores as feature representations, a Chaincodecentric method is used for minutia extraction.This is then used in the" FDivergence AdaFactor-centric Snake Active Contour Model (FDAF-SACM)" for contour extraction.Key features are then retrieved using KCP-DCNN for efficient classification.The user is authenticated if the categorization output is accurate after the Quick Response (QR) code produced from the retrieved points has been confirmed.A user identification recognition accuracy of 98.181% is attained by the created model.In order to improve the "Multimodal Biometric" (MB) system's authentication rate, the suggested approach makes use of a biosensor.