Recent advancements in deep learning have revolutionized technology and security measures, necessitating robust identification methods. Biometric approaches, leveraging personalized characteristics, offer a promising solution. However, Face Recognition Systems are vulnerable to sophisticated attacks, notably face morphing techniques, enabling the creation of fraudulent documents. In this study, we introduce a novel quadruplet loss function for increasing the robustness of face recognition systems against morphing attacks. Our approach involves specific sampling of face image quadruplets, combined with face morphs, for network training. Experimental results demonstrate the efficiency of our strategy in improving the robustness of face recognition networks against morphing attacks.

翻译：深度学习的最新进展彻底改变了技术和安全措施，从而需要强大的识别方法。利用个性化特征的生物特征方法提供了一种有前景的解决方案。然而，人脸识别系统容易受到复杂攻击的影响，尤其是人脸形变技术，该技术可生成伪造证件。在本研究中，我们引入了一种新颖的四元组损失函数，以提高人脸识别系统对形变攻击的鲁棒性。我们的方法涉及对人脸图像四元组进行特定采样，并结合人脸形变用于网络训练。实验结果证明了我们的策略在提升人脸识别网络对形变攻击鲁棒性方面的有效性。