Impact of Image Context for Single Deep Learning Face Morphing Attack Detection

The increase in security concerns due to techno- logical advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteris- tics 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.