Impact of Image Context for Single Deep Learning Face Morphing Attack Detection
This work addresses security vulnerabilities in biometric systems for users of face recognition technology, but it appears incremental as it focuses on optimizing existing alignment methods rather than introducing a new detection paradigm.
This study tackled the problem of face morphing attack detection in face recognition systems by investigating how image alignment settings affect detection performance, and it suggested optimal alignment conditions based on analyzing the interconnections between face contour and image context.
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.