Graph Neural Networks and Transformer-Based Deep Multi-Modal Fusion for Face Anti-Spoofing with Reinforcement Learning

The protection of biometric systems from presentation attacks involving printed photos, video replays and 3D masks depends on face anti-spoofing technology. A novel proposed work based on Graph Neural Networks (GNNs), Transformer-based feature extraction alongside Reinforcement Learning (RL) enables dynamic multi-modal (RGB, depth, infrared) data fusion for advanced spoof detection. The Proposed work executes three interconnected components which include GNN for complex inter-modal relationship understanding and Transformers for global dependency detection and RL for real-time fusion strategy optimization. The proposed work shows superior performance across three popular datasets including CASIA-SURF, Replay-Attack, and OULU-NPU by achieving Half Total Error Rates (HTER) of 6.9%, 9.8% and 6.2% respectively while producing results better than existing methods by a wide margin of 3.2%. Ablation tests prove the significant contribution of GNNs to the system by revealing a 1.8% HTER increase but RL enablesthe system to function with 0.6% worse results. The proposed work demonstrates 3.9% Attack Presentation Classification Error Rate (APCER) and 3.8% Bona Fide Presentation Classification Error Rate (BPCER) on CASIA-SURF along with a 3.9% ACER which proves its ability to detect various attack types. The study demonstrates how using relational modeling with global context learning and adaptive fusion efficiently supports secure face authentication processes.