The increase in security concerns due to technological advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteristics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.

翻译：技术进步带来的安全担忧日益增加，促使利用生理或行为特征增强识别的生物特征方法普及。人脸识别系统虽已广泛应用，但仍易受图像处理技术（如人脸变形攻击）的影响。本研究探讨输入图像的对齐设置对深度学习人脸变形检测性能的影响。我们分析了面部轮廓与图像上下文之间的关联，并提出了人脸变形检测的最佳对齐条件。