Face Recognition System Using Machine Learning in Banking Sector

Face Recognition-Based Authentication and Banking System that combines the power of machine learning, computer vision, and web development technologies to deliver a secure, convenient, and scalable banking platform. The core objective of this project is to implement a facial recognition mechanism that can accurately identify and verify users for secure login, registration, and financial transactions. At its foundation, the system utilizes OpenCV for image processing and facial detection, in conjunction with the Local Binary Patterns Histograms (LBPH) algorithm for training and recognizing faces. This algorithm is specifically chosen due to its robustness in handling variations in lighting and facial expressions, making it well-suited for real-world applications. The backend of the system is constructed using MySQL, a reliable and scalable relational database management system. MySQL is used to manage user data, including personal information, account numbers, balances, transaction logs, and Know Your Customer (KYC) verification status. Each user's data is securely stored and queried through a well-structured API built with the Flask web framework, which serves as the bridge between the front-end user interface and backend operations.