Design of Cabinet Monitoring and Management System Based on Two-Factor Biometric Authentication

With the increasing demand for information security, two-factor biometric authentication technology has been widely applied in identity verification. This study integrates face recognition and fingerprint recognition to design a hybrid authentication system, aiming to enhance the security and stability of cabinet monitoring and management systems. Experimental results demonstrate that the CNN+SVM hybrid authentication system achieves an accuracy rate of 99.35%, significantly outperforming other algorithms. In terms of false acceptance rate (FAR), the CNN+SVM combination exhibits a rate of 0.98%, which is lower than other configurations and shows strong anti-spoofing capability. Simultaneously, the system maintains a false rejection rate (FRR) of 1.05%, indicating low risk of erroneous rejection. The proposed two-factor authentication system demonstrates superior performance in accuracy, robustness, and response speed, possessing significant practical value and application prospects.