University of Twente Student Theses
As of Friday, 8 August 2025, the current Student Theses repository is no longer available for thesis uploads. A new Student Theses repository will be available starting Friday, 15 August 2025.
Morphing detection based on regional analysis of local frequency content
Meijer, J.J.W. (2020) Morphing detection based on regional analysis of local frequency content.
PDF
3MB |
Abstract: | Face recognition software is known to be vulnerable to a presentation attack in the form of face morphing. Face morphing detection is an active field of research. Creat- ing strong face morphing detection algorithms will make face recognition software more robust. This paper inves- tigates how regional analysis of the frequency spectrum of face images can be used to detect morphs in both a dif- ferential and non-differential setting. Three methods are explained and assessed for their performance. The first method utilizes the Kullback Leibler Divergence. The sec- ond is a Support Vector Machine (SVM). The third a Deep Feed Forward Neural Network (DFF). The latter two are trained on the frequency spectrum. The Kullback Leibler Divergence proved to be not discriminate enough to clas- sify morphs. Both the SVM and DFF were able to detect morphs with an accuracy of around 80%. |
Item Type: | Essay (Bachelor) |
Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
Subject: | 54 computer science |
Programme: | Computer Science BSc (56964) |
Link to this item: | https://purl.utwente.nl/essays/82206 |
Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Repository Staff Only: item control page