GazeHawkAI: Context-Aware Entropy-Guided Adaptive Fusion for Multi-Branch Anti-Spoofing Face Recognition
Face recognition systems are vulnerable to presentation attacks including printed photographs, mobile-screen replay, and video replay. Existing multi-modal anti-spoofing systems apply fixed branch weights during score fusion, which degrade under varying environmental conditions such as low illumination, motion blur, or extreme head pose. We propose GazeHawkAI, a three-branch anti-spoofing framework incorporating a novel Context-Aware Entropy-Guided Adaptive Fusion (CEAF) layer that dynamically re-weights branch contributions using Shannon entropy of per-branch score histories and real-time environmental quality metrics. Branch A performs ArcFace identity verification; Branch B analyses eye dynamics through Eye Aspect Ratio (EAR), blink detection, and a randomised two-step gaze challenge; Branch D evaluates face depth and texture via MediaPipe landmark geometry, Local Binary Pattern (LBP) entropy, Fast Fourier Transform (FFT) moiré detection, and a temporal video-replay detector based on pixel-variance decomposition. The CEAF layer computes per-branch confidence as Qi×Ci, normalises weights with a minimum floor of 0.05, and feeds the result to a weighted geometric-mean fusion. Benchmark testing on NUAA, Replay-Attack, and MSU-MFSD yields HTER 12.1% on Replay-Attack. This work additionally validates on a self-collected dataset of N=21,239 samples spanning five attack categories, where CEAF reduces ACER from 45.07% to 39.63% (a 5.44 percentage-point improvement over fixed-weight fusion) and AUC from 0.585 to 0.629. The system runs in real time on a CPU-only laptop with no cloud dependency.