Face recognition and verification systems are susceptible to spoofing attacks using photographs, videos or masks. Most existing methods focus on spoofing detection in Euclidean space, and ignore the features' manifold structure and interrelationships, thus limiting their capabilities of discrimination and generalization. In this paper, we propose a relativity representation on Riemannian manifold for face spoofing detection. The relativity representation improves generalization capability while ensuring discriminability, at both levels of feature description and classification score. The feature-level relativity representation generalizes information by modeling interrelationships among basic features, and would not depend too much on characteristics of a particular dataset. The score-level relativity representation makes decisions relatively, not absolutely, according to interrelationships (via Riemannian metric) and competitions (via example reweighting) among data samples on Riemannian manifold. The discriminability is ensured by the high-order nature of the feature-level relativity representation as well as Riemannian reweighted discriminative learning of the score-level relativity representation. Moreover, we integrate an attack-sensitive SVM classifier in Euclidean space to improve spoofing detection. Experiments demonstrate the effectiveness of our method on both intra-dataset and cross-dataset testing.

中文翻译：

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使用黎曼流形上的相对论表示进行人脸欺骗检测


人脸识别和验证系统很容易受到使用照片、视频或面具的欺骗攻击。大多数现有方法侧重于欧几里得空间中的欺骗检测，而忽略了特征的流形结构和相互关系，从而限制了它们的区分和泛化能力。在本文中，我们提出了用于人脸欺骗检测的黎曼流形的相对论表示。相对论表示提高了泛化能力，同时确保了特征描述和分类得分两个层面的可辨别性。特征级相对性表示通过对基本特征之间的相互关系进行建模来概括信息，并且不会过多依赖于特定数据集的特征。分数级相对论表示根据黎曼流形上数据样本之间的相互关系（通过黎曼度量）和竞争（通过示例重新加权）相对地而不是绝对地做出决策。可辨别性是通过特征级相对论表示的高阶性质以及分数级相对论表示的黎曼重新加权判别学习来保证的。此外，我们在欧几里德空间中集成了攻击敏感的 SVM 分类器，以改进欺骗检测。实验证明了我们的方法在数据集内和跨数据集测试上的有效性。