Face spoofing detection via ensemble of classifiers toward low-power devices

Abstract

Facial biometrics tend to be spontaneous, instinctive and less human intrusive. It is regularly employed in the authentication of authorized users and personnel to protect data from violation attacks. A face spoofing attack usually comprises the illegal attempt to access valuable undisclosed information as a trespasser attempts to impersonate an individual holding desirable authentication clearance. In search of such violations, many investigators have devoted their efforts to studying either visual liveness detection or patterns generated during media recapture as predominant indicators to block spoofing violations. This work contemplates low-power devices through the aggregation of Fourier transforms, different classification methods and handcrafted descriptors to estimate whether face samples correspond to falsification attacks. To the best of our knowledge, the proposed method consists of low computational cost and is one of the few methods associating features derived from both spatial and frequency image domains. We conduct experiments on recent and well-known datasets under same and cross-database settings with artificial neural networks, support vector machines and partial least squares ensembles. Results show that although our methodology is geared for resource-limited single-board computers, it can produce significant results, outperforming state-of-the-art approaches.