In recent years, to solve the problem of face spoofing, momentous work has been done in this field, but still, there is a need for establishing counter measures to the biometric spoofing attacks. Although trained and evaluated on different databases, impressive results have been achieved in existing face anti‐spoofing techniques, but biometric authentication is a very significant problem as imposters are using lots of reconstructed samples or fake synthetic material or structure that can be used for various attack purposes. For the first time, to the best of our knowledge, this paper explains the security for face anti‐spoofing detection using linear discriminant analysis and validates the results by calculating HTER and accuracy on different databases (i.e., REPLAY ATTACK and CASIA). The proposed model, that is, three‐tier face anti‐spoofing detection model (3T‐FASDM), is used for the detection of the fake biometric user and works well for real‐time applications. The proposed methods tested on a set of state‐of‐the‐art anti‐spoofing features for the face mode gives a very low degree of complexity as 26 general image quality measures are applied to differentiate among legitimate and imposter samples. The outcomes obtained from publically available data show that this technique has improved performance and accuracy by analyzing the HTER and machine learning classifiers that are helpful to differentiate among real and fake traits.

中文翻译：

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3T‐FASDM：使用支持向量机的基于线性判别分析的三层人脸反欺骗检测模型

近年来，为了解决面部欺骗的问题，已经在该领域中进行了重要的工作，但是仍然需要针对生物特征欺骗攻击建立对策。尽管在不同的数据库上进行了训练和评估，但现有的面部防欺骗技术已经取得了令人瞩目的结果，但是生物识别技术是一个非常重要的问题，因为冒名顶替者正在使用许多可重构的样本或可用于各种攻击的假合成材料或结构目的。据我们所知，本文首次解释了使用线性判别分析进行人脸反欺骗检测的安全性，并通过在不同数据库（即REPLAY ATTACK和CASIA）上计算HTER和准确性来验证结果。提出的模型，即 三层人脸反欺骗检测模型（3T-FASDM）用于检测假生物特征用户，并且在实时应用中效果很好。在针对脸部模式的一组最先进的反欺骗功能上测试的拟议方法的复杂度非常低，因为采用了26种常规图像质量度量来区分合法样本和冒名顶替者。从公开数据中获得的结果表明，该技术通过分析HTER和机器学习分类器（有助于区分真假特征）提高了性能和准确性。在针对脸部模式的一组最先进的反欺骗功能上测试的拟议方法的复杂度非常低，因为采用了26种常规图像质量度量来区分合法样本和冒名顶替者。从公开数据中获得的结果表明，该技术通过分析HTER和机器学习分类器（有助于区分真假特征）提高了性能和准确性。在针对脸部模式的一组最先进的反欺骗功能上测试的拟议方法的复杂度非常低，因为采用了26种常规图像质量度量来区分合法样本和冒名顶替者。从公开数据中获得的结果表明，该技术通过分析HTER和机器学习分类器（有助于区分真假特征）提高了性能和准确性。