Nowadays, the adoption of face recognition for biometric authentication systems is usual, mainly because this is one of the most accessible biometric modalities. Techniques that rely on trespassing these kind of systems by using a forged biometric sample, such as a printed paper or a recorded video of a genuine access, are known as presentation attacks, but may be also referred in the literature as face spoofing. Presentation attack detection is a crucial step for preventing this kind of unauthorized accesses into restricted areas and/or devices. In this paper, we propose a novel approach which relies in a combination between intrinsic image properties and deep neural networks to detect presentation attack attempts. Our method explores depth, salience and illumination maps, associated with a pre-trained Convolutional Neural Network in order to produce robust and discriminant features. Each one of these properties are individually classified and, in the end of the process, they are combined by a meta learning classifier, which achieves outstanding results on the most popular datasets for PAD. Results show that proposed method is able to overpass state-of-the-art results in an inter-dataset protocol, which is defined as the most challenging in the literature.
翻译:目前,对生物鉴别认证系统的表面识别是常见的,这主要是因为这是最容易获得的生物鉴别模式之一。依靠使用伪造的生物鉴别样本(如印刷纸或真正访问的录音录像)侵入这些系统的技术,被称作演示攻击,但在文献中也可称为表面假冒。演示攻击探测是防止这种未经授权进入禁区和/或装置的关键步骤。在本文中,我们提议一种新颖的方法,它依靠内在图像特性和深神经网络的结合,以探测攻击的图案。我们的方法探索深度、突出度和照明图,与预先训练的进化神经网络相关,以产生强健和分裂的特征。这些特性都是个别分类的,在过程结束时,由一个元学习分类器加以结合,在PAD最受欢迎的数据集上取得突出的结果。结果显示,拟议的方法能够在一个相互数据配置协议中超越状态结果,该协议的定义是最具挑战性的文献。