University of Twente Student Theses

Login

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.

[img] 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:http://purl.utwente.nl/essays/82206
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page