Two-Factor Biometric Identity Verification System for the Human-Machine System Integrated Deep Learning Model

Abstract

The Human-Machine Identity Verification System based on Deep Learning offers a robust and automated approach to identity verification, leveraging the power of deep learning algorithms to enhance accuracy and security. This paper focused on the biometric-based authentical scheme with Biometric Recognition for the Huma-Machinary Identification System. The proposed model is stated as the Two-Factor Biometric Authentication Deep Learning (TBAuthDL). The proposed TBAuthDL model uses the iris and fingerprint biometric data for authentication. TBAuthDL uses the Weighted Hashing Cryptographic (WHC) model for the data security. The TBAuthDL model computes the hashing factors and biometric details of the person with WHC and updates to the TBAuthDL. Upon the verification of the details of the assessment is verified in the Human-Machinary identity. The simulation analysis of TBAuthDL model achieves a higher accuracy of 99% with a minimal error rate of 1% which is significantly higher than the existing techniques. The performance also minimizes the computation and processing time with reduced complexity.