演示攻击检测(PAD)算法已成为面部识别系统安全使用的不可或缺的要求。随着人脸识别算法和应用从约束到无约束环境以及多光谱场景的增加,演示攻击检测算法还必须增加其范围和有效性。重要的是要认识到,PAD算法不仅对一种环境或条件有效,而且可推广到呈现给面部识别算法的多种变量。有了这个动机,作为第一个贡献,本文提出了一种统一的PAD算法,用于打印照片等不同类型的攻击,视频的回放,3D遮罩,硅胶面罩,蜡脸。所提出的算法利用来自传感器的小波分解的原始输入图像和面部区域数据的组合来检测输入图像是真实的还是被攻击的。本文的第二个贡献是收集了NIR频谱中的大型演示攻击数据库,包含来自两个种族的个人的图像。该数据库包含500个打印攻击视频,其中包括NIR光谱中的大约1,00,000帧。对NIR图像以及从现有基准数据库获得的可见光谱图像的算法的广泛评估表明,所提出的算法产生了最先进的结果,并且超越了几种复杂和最先进的算法。例如,在基准数据集上,即CASIA-FASD,重播-攻击,和MSU-MFSD,该算法的最大误差为0.92%,显著低于最先进的攻击检测算法。
Presentation attack detection (PAD) algorithms have become an integral requirement for the secure usage of face recognition systems. As face recognition algorithms and applications increase from constrained to unconstrained environments and in multispectral scenarios, presentation attack detection algorithms must also increase their scope and effectiveness. It is important to realize that the PAD algorithms are not only effective for one environment or condition but rather be generalizable to a multitude of variabilities that are presented to a face recognition algorithm. With this motivation, as the first contribution, the article presents a unified PAD algorithm for different kinds of attacks such as printed photos, a replay of video, 3D masks, silicone masks, and wax faces. The proposed algorithm utilizes a combination of wavelet decomposed raw input images from sensor and face region data to detect whether the input image is bonafide or attacked. The second contribution of the article is the collection of a large presentation attack database in the NIR spectrum, containing images from individuals of two ethnicities. The database contains 500 print attack videos which comprise approximately 1,00,000 frames collectively in the NIR spectrum. Extensive evaluation of the algorithm on NIR images as well as visible spectrum images obtained from existing benchmark databases shows that the proposed algorithm yields state-of-the-art results and surpassed several complex and state-of-the-art algorithms. For instance, on benchmark datasets, namely CASIA-FASD, Replay-Attack, and MSU-MFSD, the proposed algorithm achieves a maximum error of 0.92% which is significantly lower than state-of-the-art attack detection algorithms.