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($\mu$E) 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 $\mu$E. 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 $\mu$E 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.

由于生物特征安全系统中侵入性的脆弱性，近年来，面部重放攻击已经成为人们关注的话题。为了构建健壮的生物识别系统，研究人员已经开发出许多保护措施，以消除诸如打印和重放攻击之类的欺骗活动。本文对多维傅立叶变换在重放攻击中的应用进行了全面的研究。由于多维傅立叶变换中的较高频率包含主要特征变化，因此人脸的活泼主要反映在高频频谱中。自发的面部表情，例如微表情（$\mu$E）带有详细的内部面部变化。在这种新颖的方法中，修改后的高频描述符用于正确区分实况和伪造的面部视频流。该描述符对于面部变化尤其有效$\mu$E.在较高的频谱中，包含噪声以及特征变化是微不足道的。因此，该方法在预处理阶段不仅提取了具有$\mu$E会发生变化，但也会滤除带有任何突然的表情变化（宏观表情）或尖峰噪声的帧。然后，将选定的帧序列馈入多维傅里叶平面，以检测活动度。实验是在自行创建的数据集上进行的，也正在标准回放攻击数据集上进行测试。通过拟议的反欺骗方法获得的结果令人满意，并被证明具有统计学意义。