Fine-Grained Annotation for Face Anti-Spoofing
Xu Chen Yunde Jia Yuwei Wu
Face anti-spoofing plays a critical role in safeguarding facial recognition
systems against presentation attacks. While existing deep learning methods show
promising results, they still suffer from the lack of fine-grained annotations,
which lead models to learn task-irrelevant or unfaithful features. In this
paper, we propose a fine-grained annotation method for face anti-spoofing.
Specifically, we first leverage the Segment Anything Model (SAM) to obtain
pixel-wise segmentation masks by utilizing face landmarks as point prompts. The
face landmarks provide segmentation semantics, which segments the face into
regions. We then adopt these regions as masks and assemble them into three
separate annotation maps: spoof, living, and background maps. Finally, we
combine three separate maps into a three-channel map as annotations for model
training. Furthermore, we introduce the Multi-Channel Region Exchange
Augmentation (MCREA) to diversify training data and reduce overfitting.
Experimental results demonstrate that our method outperforms existing
state-of-the-art approaches in both intra-dataset and cross-dataset
evaluations.