摘要
随着可见光-红外双模相机在视频监控中的广泛应用,跨模态人脸识别也成为计算机视觉领域的研究热点,而将近红外域人脸图像转化为可见光域人脸图像是跨模态人脸识别中的关键问题,在刑侦安防领域有着重要研究价值。针对近红外人脸图像在着色过程中面部轮廓易被扭曲、肤色还原不真实等问题,本文提出了一种双重对比学习框架下的近红外-可见光人脸图像转换方法。该方法构建了基于StyleGAN2结构的生成器网络并将其嵌入到双重对比学习框架下,利用双向的对比学习挖掘人脸图像的精细化表征。同时,本文设计了一种面部边缘增强损失,利用从源域图像中提取的面部边缘信息进一步强化生成人脸图像中的面部细节、提高人脸图像的视觉效果。最后,在NIR-VIS Sx1和NIR-VIS Sx2数据集上的实验表明,与近期的主流方法相比,本文方法生成的可见光人脸图像更加贴近真实图像,能够更好地还原人脸图像的面部边缘细节和肤色信息。
With the wide application of visible-infrared dual-mode cameras in video surveillance,cross-modal face recognition has become a research hotspot in the field of computer vision.The translation of NIR domain face images into VIS domain face images is a key problem in cross-modal face recognition,which has important research value in the fields of criminal investigation and security.Aiming at the problems that facial contours are easily distorted and skin color restoration is unrealistic during the coloring process of NIR face images,this paper proposes a NIR-VIS face images translation method under a dual contrastive learning framework.This method constructs a generator network based on the StyleGAN2 structure and embeds it into the dual contrastive learning framework to exploit the fine-grained characteristics of face images using bidirectional contrastive learning.Meanwhile,a facial edge enhancement loss is designed to further enhance the facial details in the generated face images and improve the visual effects of the face images using the facial edge information extracted from the source domain images.Finally,experiments on the NIR-VIS Sx1 and NIR-VIS Sx2 datasets show that,compared with the recent mainstream methods,the VIS face images generated by this method are closer to the real images and possesses more facial edge details and skin color information of the face images.
作者
孙锐
单晓全
孙琦景
韩春军
张旭东
Sun Rui;Shan Xiaoquan;Sun Qijing;Han Chunjun;Zhang Xudong(School of Computer and Information,Hefei University of Technology,Hefei,Anhui 230009,China;Anhui Province Key Laboratory of Industry Safety and Emergency Technology,Hefei,Anhui 230009,China;Science and Technology Information Section of Bengbu Public Security Bureau,Bengbu,Anhui 233040,China)
出处
《光电工程》
CAS
CSCD
北大核心
2022年第4期26-38,共13页
Opto-Electronic Engineering
基金
国家自然科学基金面上项目(61471154,61876057)
安徽省重点研发计划-科技强警专项(202004d07020012)。
