Face morphing attack detection (MAD) algorithms have become essential to overcome the vulnerability of face recognition systems. To solve the lack of large-scale and public-available datasets due to privacy concerns and restrictions, in this work we propose a new method to generate a synthetic face morphing dataset with 2450 identities and more than 100k morphs. The proposed synthetic face morphing dataset is unique for its high-quality samples, different types of morphing algorithms, and the generalization for both single and differential morphing attack detection algorithms. For experiments, we apply face image quality assessment and vulnerability analysis to evaluate the proposed synthetic face morphing dataset from the perspective of biometric sample quality and morphing attack potential on face recognition systems. The results are benchmarked with an existing SOTA synthetic dataset and a representative non-synthetic and indicate improvement compared with the SOTA. Additionally, we design different protocols and study the applicability of using the proposed synthetic dataset on training morphing attack detection algorithms.

翻译：人脸融合攻击检测算法已成为克服人脸识别系统脆弱性的关键手段。针对因隐私顾虑和限制导致大规模公开数据集匮乏的问题，本研究提出一种新方法，用于生成包含2450个身份和超过10万张融合图像的合成人脸融合数据集。该合成人脸融合数据集具有样本质量高、融合算法类型多样、可同时适用于单样本与差分融合攻击检测算法等独特优势。实验中，我们通过人脸图像质量评估和脆弱性分析，从生物特征样本质量及人脸识别系统受融合攻击的潜在风险两个维度，对所提出的合成人脸融合数据集进行评估。结果与现有最先进的合成数据集及代表性非合成数据集进行基准比较，表明本数据集相较最先进方法有所改进。此外，我们设计了不同实验协议，并研究了所提合成数据集在训练融合攻击检测算法中的适用性。