Face morphing attacks have emerged as a potential threat, particularly in automatic border control scenarios. Morphing attacks permit more than one individual to use travel documents that can be used to cross borders using automatic border control gates. The potential for morphing attacks depends on the selection of data subjects (accomplice and malicious actors). This work investigates lookalike and identical twins as the source of face morphing generation. We present a systematic study on benchmarking the vulnerability of Face Recognition Systems (FRS) to lookalike and identical twin morphing images. Therefore, we constructed new face morphing datasets using 16 pairs of identical twin and lookalike data subjects. Morphing images from lookalike and identical twins are generated using a landmark-based method. Extensive experiments are carried out to benchmark the attack potential of lookalike and identical twins. Furthermore, experiments are designed to provide insights into the impact of vulnerability with normal face morphing compared with lookalike and identical twin face morphing.

翻译：面部形变攻击已成为一种潜在威胁，尤其在自动边境管控场景中。此类攻击允许多个个体使用同一旅行证件通过自动边境检查闸机。形变攻击的可行性取决于数据对象（共谋者与恶意行为者）的选择。本研究探讨以相似面孔及同卵双胞胎作为面部形变生成源。我们系统性地评估了人脸识别系统（FRS）对相似面孔与同卵双胞胎形变图像的脆弱性。为此，我们基于16对同卵双胞胎及相似面孔数据对象构建了新的面部形变数据集，并采用基于地标的方法生成形变图像。通过大量实验，我们基准测试了相似面孔与同卵双胞胎的攻击潜力，并进一步设计实验以揭示普通面部形变与相似面孔/同卵双胞胎面部形变的脆弱性差异。