With the hyperspectral sensor technology evolving and becoming more cost-effective, hyperspectral imaging offers new opportunities for robust face recognition. Hyperspectral face cubes contain much more spectral information than face images from common RGB color cameras. Hyperspectral face recognition is robust to the impacts, such as illumination, pose, occlusion, and spoofing, which can heavily avoid the limitations of the visible-image-based face recognition.

In this paper, we summarize the spectrum properties of hyperspectral face cubes and survey the hyperspectral face recognition methods in the literature. We categorize them into major groups for better understanding. We overview the existing hyperspectral face datasets, and establish our own dataset. We also discuss efficient neural networks used for mobile face recognition and conduct experiments on mobile hyperspectral face recognition. Results show that under harsh conditions like large illumination changing and pose variation, hyperspectral-cube-based methods have higher recognition accuracy than visible-image-based methods. Finally, we deliver insightful discussions and prospects for future works on mobile hyperspectral face recognition.

随着高光谱传感器技术的发展并变得更具成本效益，高光谱成像为稳固的人脸识别提供了新的机会。高光谱人脸立方体比来自普通RGB彩色摄像机的人脸图像包含更多的光谱信息。高光谱人脸识别对于诸如照明，姿势，遮挡和欺骗等冲击具有鲁棒性，可以极大地避免基于可见图像的人脸识别的局限性。

在本文中，我们总结了高光谱人脸立方体的光谱特性，并综述了文献中的高光谱人脸识别方法。我们将它们分为几个主要类别，以便更好地理解。我们概述了现有的高光谱人脸数据集，并建立了自己的数据集。我们还将讨论用于移动人脸识别的高效神经网络，并对移动高光谱人脸识别进行实验。结果表明，在苛刻的条件下，例如大的照明变化和姿势变化，基于高光谱立方体的方法比基于可见图像的方法具有更高的识别精度。最后，我们为移动高光谱人脸识别的未来工作提供了有见地的讨论和前景。