Novel framework for multimodal biometric image authentication using visual share neural network

摘要

Over the years world is revolutionized with the advent of technology, the field of transmission too is made easier with the use of different technologies but security during transmission is still a threat. Visual cryptography (VC) is a cryptographic technique that encrypts visual information (pictures, text, etc.) where the decrypted content appears as a visual image. Visual cryptography enables to transmit images securely as well as maintains data confidentiality. The security of multimodal biometric data is a challenging task in the current world scenario as various domains are prone to more threats. In order to secure and authenticate multimodal biometric data a new framework is proposed. The proposed framework provides an optimal solution for securing and authenticating images during transmission. In this paper, three types of biometric inputs were resolute and taken as input: Fingerprint, Face and Iris images. A new shuffling approach which is based on a pixel element is used for creating shares for every plane. These shares are used in the decoding process for reconstruction. The three reconstructed images are passed through the bilateral filter in order to eliminate noise and preserve edges. Each biometric data (BD) utilizes different image segmentation technique: (1) Binary segmentation for finger-print image, (2) face segmentation for face image, and (3) Daugman's integrodifferential operational for iris image. Similarly, feature extraction modules are executed on three segmented images. These features extracted from the reconstructed images are used to train the Visual Sharing Neural Network (VSNN) along with Recurrent Neural Network-Bidirectional Long Short-Term Memory (RNN-BiLSTM) model. In the testing process, the input image is classified using VSNN and it checks for matching BD in the database before providing the authentication to individual. The simulation result, of the proposed module, gives the PSNR as 38.58, MSE as 9, and NCC as 1, 95 of accuracy, 100 of sensitivity, and 5 of error rate, 98.8 of identification rate, 0.45 of false acceptance rate, and 0.97 of false rejection rate. (c) 2021 Elsevier B.V. All rights reserved.