Fine-Grained Annotation for Face Anti-Spoofing

TL;DR

This paper introduces a fine-grained annotation method for face anti-spoofing using SAM for pixel-wise segmentation and a novel augmentation technique, significantly improving model performance and robustness.

Contribution

The paper presents a new fine-grained annotation approach leveraging SAM and a multi-channel region exchange augmentation to enhance face anti-spoofing models.

Findings

Outperforms state-of-the-art methods in intra-dataset evaluations.

Achieves better cross-dataset generalization.

Reduces overfitting through novel data augmentation.

Abstract

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.