Intelligent multimodal face anti-spoofing detection system based on biometrics

Abstract

With the widespread deployment of face recognition systems, high-fidelity spoofing attacks such as photo replays, video replays, and 3D masks pose significant security threats. Current face detection approaches (e.g., CNN-based methods) often fail to adequately capture the distinct properties of different modalities during feature fusion, resulting in persistent security risks within face detection systems. This paper proposes a multi-dimensional feature fusion-based face anti-spoofing detection method based on the YOLOv8 architecture. Unlike traditional methods that rely solely on static RGB input, the paper integrates dynamic optical flow features (capturing micro-movements) with static texture analysis. By utilizing a cross-scene dataset containing diverse attack samples ,the method achieves robust detection through the fusion of spatial and temporal cues. Additionally, data augmentation strategies are employed to help the model better localize and identify targets across different scales. The proposed method exhibits strong practical value for intelligent systems in complex scenarios.