Morphing detection using local spectra

Face recognition systems are used in a variety of applications such as automated border control. Recently, it was demonstrated that such systems are highly vulnerable to presentation attacks using a morphed image based on two bona fide images. This has as consequence that illegitimate sharing of biometric passports has been made possible. For proper border security, and many other applications, it is therefore necessary to find a successful morphing attack detection system which can classify between bona fide images and morphed images. Some progress has been made already in several studies. However, a proper morphing attack detection system which performs well across different databases of images and morphing pipelines has not been found yet. In this research, the effect of face morphing on local stretches and compressions of frequencies is investigated. The focus of this research is to investigate whether Affine transformations have a traceable effect on the frequency domain. This was done in two steps. Firstly, a homogeneous and a white noise image was used in the morphing pipeline to inspect distortions made by Affine transformations. A 2-D continuous wavelet transform was applied to both images. Secondly, 1-D continuous wavelet transformations have been used on skin textures to find out whether there is a substantial shift in scales (frequencies) due to the different Affine transformations. Experimental results show a remarkable pattern appearing in the homogeneous, white noise and morphed image. However, it is found that the 1-D continuous wavelet transforms used in this research are not able to differentiate bona fide images and morphed images.