Face Liveness Detection Using RGB and Thermal Image Fusion

Face detection with visible-spectrum cameras can capture facial features, but it often fails to distinguish live subjects from spoof sources such as photographs, masks, or statues. Previous approaches based on texture, motion, or physiological cues are sensitive to illumination changes and show limited robustness against spoofing attacks. Thermal imaging helps overcome these limitations by detecting heat emissions, naturally excluding spoof faces. This study proposes a hybrid approach that fuses the edge information of RGB images with corresponding thermal images using a custom ARISTOF dataset containing live and spoof faces. The fused images are first evaluated using the YOLOv8-Face model to compare face detection performance across RGB, thermal, and fused modalities. The results show that the proposed method enhances the face detection accuracy of thermal images. The fused images are subsequently used to train a YOLOv8-Face model for live and spoof classification, demonstrating that the proposed multimodal fusion effectively supports robust face liveness detection.