Spoofed replay attack detection by Multidimensional Fourier transform on facial micro-expression regions

Facial replay attacks have been a topic of interest in recent past due to the vulnerability of intrusive nature in biometric security systems. In order to build a robust biometric system many safeguard approaches have already been developed by the researchers to nullify spoofing activities like print and replay attacks. This paper proposes a comprehensive study on the application of Multidimensional Fourier transform to combat replay attacks. Since the higher frequency in Multidimensional Fourier transform contains the major feature variations, liveness of a face is mostly reflected in the high frequency spectrum. The spontaneous facial expressions like micro-expression(μE) carries the detailed inner facial variations. In this novel approach a modified high frequency descriptor is used for proper discrimination between a live and fake facial video streams. The descriptor in particular works efficiently for a change in facial μE. Inclusion of noise along with the feature variation is trivial in higher frequency spectrum. The method, therefore, during the pre-processing phase not only extracts the video frames with major μE changes but also filters out frames carrying any abrupt expression change (macro expression) or spike noise. The selected frame sequence are thereafter fed into the multi dimensional Fourier plane in order to detect the liveness. The experiment is performed on the self created dataset and also being tested on standard play back attack dataset. The result obtained by the proposed anti spoofing approach is satisfactory and verified to be statistically significant.