Combining Identity Features and Artifact Analysis for Differential Morphing Attack Detection

Due to the importance of the Morphing Attack, the development of new and accurate Morphing Attack Detection (MAD) systems is urgently needed by private and public institutions. In this context, D-MAD methods, i.e. detectors fed with a trusted live image and a probe tend to show better performance with respect to S-MAD approaches, that are based on a single input image. However, D-MAD methods usually leverage the identity of the two input face images only, and then present two main drawbacks: they lose performance when the two subjects look alike, and they do not consider potential artifacts left by the morphing procedure (which are instead typically exploited by S-MAD approaches). Therefore, in this paper, we investigate the combined use of D-MAD and S-MAD to improve detection performance through the fusion of the features produced by these two MAD approaches.