Real-Time Access Control System with YOLOv11-Based Face and Blink Detection - Namira Nur Rifani; RD. Kusumanto; Nyayu Latifah Husni (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2025)
This study presents a real-time smart access control system that combines facial recognition with blink-based liveness detection to strengthen security and reduce spoofing risks. The main purpose is to provide a lightweight and efficient method that verifies both identity and physical presence in real time. The system employs two YOLOv11 models: one for detecting facial regions and another for distinguishing eye states through “open” and “closed” transitions. Identity verification is carried out by comparing facial embeddings using Euclidean distance. A private dataset was collected for facial images, while blink data was obtained from a public source, both annotated in YOLO format. After 100 epochs, the face detection model achieved 0.999 precision, 1.000 recall, 0.995 mAP50, and 0.868 mAP50–90, while the blink detection model recorded 0.959 precision, 0.962 recall, 0.967 mAP50, and 0.678 mAP50–90. These outcomes confirm that the objectives were achieved, demonstrating a practical and reliable biometric authentication solution with integrated liveness verification. The system also offers scalability for future multi-modal applications.