Quadruplet Loss For Improving the Robustness to Face Morphing Attacks
This work addresses security issues in biometric identification for applications like document verification, but it is incremental as it builds on existing loss function methods.
The paper tackles the vulnerability of face recognition systems to morphing attacks by introducing a novel quadruplet loss function, which improves robustness as demonstrated in experiments.
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.