文献类型：期刊文献

英文题名：Hyperspectral-cube-based mobile face recognition: A comprehensive review

作者：Zhang, Xianyi[1];Zhao, Haitao[1]

机构：[1]East China Univ Sci & Technol, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2021

卷号：74

起止页码：132

外文期刊名：INFORMATION FUSION

收录：;EI(收录号：20211910319404);WOS:【SCI-EXPANDED(收录号:WOS:000659136200010)】；

基金：This research is sponsored by National Natural Science Foundation of China (61973122).

语种：英文

外文关键词：Hyperspectral face cube; Mobile face recognition; Hyperspectral face dataset; Lightweight neural network; Convolutional kernel

摘要：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.