TRI-GATE: A Tri-Modal Anti-Spoofing System for Gate Access Using Vehicle, License Plate, and Face Recognition

Vehicle gate access, in general, still relies heavily on manual inspection of identification cards and visual verification by security guards, which is slow, tedious, and susceptible to spoofing. Single-modality, computerized systems that utilize license plates, vehicle appearance, and facial recognition can partially alleviate this difficulty. Still, they are prone to spoofing and generally perform poorly in real-world scenarios (e.g., glare, occlusion, and tinted glass). This study presents TRI-GATE, a tri-modal anti-spoofing framework that unifies vehicle, license plate, and face recognition within a single, real-time decision pipeline. The system employs YOLOv4-tiny for vehicle detection and a MobileNetV2-based classifier for make–model recognition, a retrained MTCNN and LPRNet pair for license plate detection and recognition on Saudi-specific datasets (17,000 images for detection and 35,000 for recognition), and RetinaFace with InsightFace embeddings, along with a linear SVM, for driver identification. An IoU-based best-frame selection scheme reduces latency by forwarding only the most informative frame to the recognition modules. Score-level fusion is then performed by a linear SVM that learns the relative importance of each modality for the final access decision. Evaluated on a dedicated tri-modal dataset, TRI-GATE achieves 97% gate-level accuracy with an end-to-end latency of 66 ms per frame (≈ 15.15 FPS), and demonstrates robust performance in a real-world gate-like deployment, substantially improving both security and operational efficiency over existing single- and bi-modal solutions.