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.

面部生物识别技术通常是自发的，本能的，并且对人类的干扰较小。它通常用于授权用户和人员的身份验证中，以保护数据免受违规攻击。面部欺骗攻击通常包括非法尝试访问有价值的未公开信息，因为侵入者试图假冒持有所需身份验证许可的个人。为了寻找这种违规行为，许多调查人员致力于研究视觉活力检测或媒体捕获期间产生的模式，以作为阻止欺骗性违规行为的主要指标。这项工作通过聚集傅立叶变换，不同的分类方法和手工描述符来估计低功耗设备，以估计面部样本是否对应于伪造攻击。据我们所知，该方法的计算成本较低，是将空间和频率图像域中的特征进行关联的少数方法之一。我们使用人工神经网络，支持向量机和偏最小二乘合集，在相同和跨数据库设置下对最近和著名的数据集进行实验。结果表明，尽管我们的方法适用于资源有限的单板计算机，但它可以产生显着的结果，胜过最先进的方法。我们使用人工神经网络，支持向量机和偏最小二乘合集，在相同和跨数据库设置下对最近和著名的数据集进行实验。结果表明，尽管我们的方法适用于资源有限的单板计算机，但它可以产生显着的结果，胜过最先进的方法。我们使用人工神经网络，支持向量机和偏最小二乘合集，在相同和跨数据库设置下对最近和著名的数据集进行实验。结果表明，尽管我们的方法适用于资源有限的单板计算机，但它可以产生显着的结果，胜过最先进的方法。