论文标题
HyperFaceNet：一种基于深融合的高光谱识别方法
HyperFaceNet: A Hyperspectral Face Recognition Method Based on Deep Fusion
论文作者
论文摘要
在可见光的光线和红色谱系病例中，面部识别已经在可见光和红外线下进行了很好的研究。但是，如何融合不同的光带，即高光谱的面部识别，仍然是一个开放的研究问题，它具有比单个乐队面部识别更丰富的信息保留和全天候功能的优势。在高光谱识别的少数作品中，传统的非深度学习技术在很大程度上使用。因此，我们在本文中深入学习高光谱的面部识别主题，并提出了一种新的融合模型（称为超肌），尤其是对于高光谱面孔。所提出的融合模型的特征是残留的密集学习，反馈样式编码器和面向识别的损失函数。在实验过程中，我们的方法被证明比使用可见光或红外线的面部识别率更高。此外，就图像质量和识别性能而言，我们的融合模型被证明优于其他通用图像融合方法，包括最先进的方法。
Face recognition has already been well studied under the visible light and the infrared,in both intra-spectral and cross-spectral cases. However, how to fuse different light bands, i.e., hyperspectral face recognition, is still an open research problem, which has the advantages of richer information retaining and all-weather functionality over single band face recognition. Among the very few works for hyperspectral face recognition, traditional non-deep learning techniques are largely used. Thus, we in this paper bring deep learning into the topic of hyperspectral face recognition, and propose a new fusion model (termed HyperFaceNet) especially for hyperspectral faces. The proposed fusion model is characterized by residual dense learning, a feedback style encoder and a recognition-oriented loss function. During the experiments, our method is proved to be of higher recognition rates than face recognition using either visible light or the infrared. Moreover, our fusion model is shown to be superior to other general-purposed image fusion methods including state-of-the-arts, in terms of both image quality and recognition performance.
