Face Morphing and Morphing Attack Detection

In modern society, biometrics is gaining more and more importance, driven by the increase in recognition performance of the systems. In some areas, such as automatic border controls, there is no alternative to the application of biometric systems. Despite all the advantages of biometric systems, the vulnerability of these still poses a problem. Facial recognition systems for example offer various attack points, like faces printed on paper or silicone masks. Besides the long known and well researched presentation attacks there is also the danger of the so-called morphing attack. The research field of morphing attacks is quite young, which is why it has only been investigated to a limited extent so far. Publications proposing algorithms for the detection of morphing attacks often lack uniform databases and evaluation methods, which leads to a restricted comparability of the previously published work. Thus, the focus of this thesis is the comprehensive analysis of different features and classifiers in their suitability as algorithms for the detection of morphing attacks. In this context, evaluations are performed with uniform metrics on a realistic morphing database, allowing the simulation of various realistic scenarios. If only the suspected morph is available, a HOG feature extraction in combination with an SVM is able to detect morphs with a D-EER ranging from 13.25% to 24.05%. If a trusted live capture image is available in addition, for example from a border gate, the deep ArcFace features in combination with an SVM can detect morphs with a D-EER ranging from 2.71% to 7.17%