In recent years, the banking sector has witnessed a significant surge in digital transactions and online banking services. As the dependency on technology increases, ensuring robust security measures becomes imperative to protect sensitive customer data and prevent fraudulent activities. This project proposes a sophisticated banking security system that utilizes face and liveness detection techniques, employing machine learning and image processing algorithms. The primary objective of this project is to enhance the security of banking transactions by introducing a multi-factor authentication system that combines facial recognition and liveness detection. The system leverages the power of machine learning algorithms, specifically convolutional neural networks (CNNs), to accurately identify and authenticate users based on their facial features. By employing deep learning techniques, the system can handle variations in facial expressions, poses, and lighting conditions, ensuring reliable and secure identification. Furthermore, liveness detection techniques are integrated into the system to prevent spoofing attempts. Through image processing algorithms and computer vision techniques, the system verifies the presence of a live person in front of the camera, mitigating the risk of identity theft and unauthorized access. By detecting and analyzing various facial cues and subtle movements, the system can differentiate between a real person and a static image or video playback. The proposed banking security system offers several advantages over traditional authentication methods. It eliminates the need for physical tokens or passwords, providing a more convenient and user-friendly experience for customers. Moreover, it enhances security by minimizing the chances of identity theft and impersonation. To evaluate the effectiveness of the system, a comprehensive dataset containing images and videos of various individuals is collected and used for training and testing the machine learning models. The system is benchmarked against existing authentication methods to assess its accuracy, efficiency, and robustness. The experimental results demonstrate the superiority of the proposed approach in terms of accuracy and security.

Face Recognition, Face Spoofing, Convolutional Neural Network (CNN) Classifier, Face Liveness Detection, Deep Learning, Image Processing, etc.