Abstract
VIS-NIR face recognition remains a challenging task due to the distinction between spectral components of two modalities and insufficient paired training data. Inspired by the CycleGAN, this paper presents a method aiming to translate VIS face images into fake NIR images whose distributions are intended to approximate those of true NIR images, which is achieved by proposing a new facial feature embedded CycleGAN. Firstly, to learn the particular feature of NIR domain while preserving common facial representation between VIS and NIR domains, we employ a general facial feature extractor (FFE) to replace the encoder in the original generator of CycleGAN. For implementing the facial feature extractor, herein the MobileFaceNet is pretrained on a VIS face database, and is able to extract effective features. Secondly, the domain-invariant feature learning is enhanced by considering a new pixel consistency loss. Lastly, we establish a new WHU VIS-NIR database which varies in face rotation and expressions to enrich the training data. Experimental results on the Oulu-CASIA NIR-VIS database and the WHU VIS-NIR database show that the proposed FFE-based CycleGAN (FFE-CycleGAN) outperforms state-of-the-art VIS-NIR face recognition methods and achieves 96.5\% accuracy.
Abstract (translated)
VIS-NIR人脸识别仍然是一项具有挑战性的任务，因为两种模式的光谱成分之间的区别和配对训练数据不足。本文在Cyclegan的启发下，提出了一种将VIS人脸图像转化为伪NIR图像的方法，该方法的目的是为了近似真实NIR图像的分布，并提出了一种新的人脸特征嵌入Cyclegan。首先，为了在保持可见光域和近红外域的共同人脸表示的同时，了解近红外域的特殊特征，我们采用了一种通用的人脸特征抽取器（ffe）来代替原Cyclegan生成器中的编码器。为了实现面部特征提取，本文在VIS面部数据库上对MobileFaceNet进行了预训练，并且能够提取有效的特征。其次，考虑到一个新的像素一致性损失，增强了区域不变特征学习。最后，我们建立了一个新的whu-vis-nir数据库，该数据库在面部旋转和表情上有所不同，以丰富训练数据。在Oulu-Casia NIR-VIS数据库和WHU-VIS-NIR数据库上的实验结果表明，所提出的基于ffe的cyclegan（ffe cyclegan）优于最先进的vis-nir人脸识别方法，精度达到96.5%。
URL
https://arxiv.org/abs/1904.09464
