Data fusion-based two-stage cascade framework for multi-modality face anti spoofing

Face anti-spoofing is an essential component in modern biometric authentication systems, ensuring that recognition technologies are not deceived by fraudulent attempts such as printed photographs, video replays, or 3D masks. This project proposes a Data Fusion-Based Two Stage Cascade Framework that integrates multiple modalities—RGB (Red, Green, Blue), Depth, and Infrared (IR)—to improve robustness and accuracy in detecting spoofing. In the first stage, deep learning models like 3D Convolutional Neural Networks (3D CNNs), CNN LSTM (Convolutional Neural Network with Long Short-Term Memory), and attention mechanisms are applied for feature extraction. In the second stage, their outputs are combined through decision fusion in a multi-stream network. The framework is evaluated on benchmark datasets like CASIA-SURF and Replay-Attack. Results show significant improvements over traditional single-modality systems, making the proposed framework suitable for real-world applications in banking, airport security, and access control systems.