Impact of Image Context for Single Deep Learning Face Morphing Attack
  Detection
Joana Pimenta Iurii Medvedev Nuno Gonçalves
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.