Enhancing Bank Locker Security: Machine Learning-Based Facial Recognition with Two-Factor Authentication

Bank locker security is vital for securing clients' important resources. In this paper, we propose an imaginative approach to improve bank locker security through the integration of machine learning-based facial acknowledgment with a two-factor confirmation framework utilizing Convolutional Neural Systems (CNN). The proposed framework points to moderating the chance of unauthorized get-to and false exercises, guaranteeing strong assurance for clients' resources. The facial acknowledgment module utilizes a state-of-the-art CNN design prepared on an assorted dataset of facial pictures, empowering the framework to precisely recognize authorized clients. The CNN's profound learning capabilities encourage the extraction of complicated facial highlights, guaranteeing solid and proficient distinguishing proof indeed in changing natural conditions. Furthermore, the two-factor confirmation component supplements the facial acknowledgment framework, supporting security through a dual-layer confirmation handle. Clients are required to verify their character through facial acknowledgment and an auxiliary verification strategy, such as a one-of-a-kind passcode or biometric identifier, to pick up and get to the bank lockers. This two-factor confirmation methodology fortifies the framework against potential breaches and pantomime assaults. Besides, persistent demonstration optimization and customary overhauls are actualized to adjust to advancing security dangers and to preserve the system's strength. Thorough testing and approval illustrate the system's vigor and precision in real-world scenarios, approving its adequacy in reinforcing bank locker security. The proposed integration of machine learning-based facial acknowledgment with two-factor verification utilizing CNN presents a noteworthy headway within the domain of bank locker security, advertising a modern, dependable, and strong arrangement to defend clients' important resources against unauthorized get to and potential security breaches.