Presentation Attack Detection (PAD) has been extensively studied, particularly in the visible spectrum. With the advancement of sensing technology beyond the visible range, multispectral imaging has gained significant attention in this direction. We present PAD based on multispectral images constructed for eight different presentation artifacts resulted from three different artifact species. In this work, we introduce Face Presentation Attack Multispectral (FPAMS) database to demonstrate the significance of employing multispectral imaging. The goal of this work is to study complementary information that can be combined in two different ways (image fusion and score fusion) from multispectral imaging to improve the face PAD. The experimental evaluation results present an extensive qualitative analysis of 61650 sample multispectral images collected for bonafide and artifacts. The PAD based on the score fusion and image fusion method presents superior performance, demonstrating the significance of employing multispectral imaging to detect presentation artifacts.

翻译：呈现攻击检测（PAD）已在可见光范围内得到广泛研究。随着传感技术超越可见光范围的发展，多光谱成像在这一方向引起了广泛关注。我们基于由三种不同伪影类型产生的八种呈现伪影所构建的多光谱图像，提出了PAD方法。本文引入人脸呈现攻击多光谱（FPAMS）数据库，以展示采用多光谱成像的重要性。本研究旨在探讨可通过两种不同方式（图像融合与分数融合）从多光谱成像中结合的补充信息，以改善人脸PAD。实验评估结果对收集自真实人脸与伪影的61650个多光谱图像样本进行了广泛的定性分析。基于分数融合与图像融合方法的PAD展现出优越性能，验证了采用多光谱成像检测呈现伪影的重要意义。