  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.
