身份保持约束下的面部图像超分辨率重建方法  

Faciad Image Super‑Resolution Reconstruction Method with Identity Preserving

作　　者：田旭 刁红军[1] 凌兴宏[1,2,3] TIAN Xu;DIAO Hongjun;LING Xinghong(School of Computer Science and Technology,Soochow University,Suzhou 215006,China;Suzhou City University,Suzhou 215104,China;Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China)

机构地区：[1]苏州大学计算机科学与技术学院,苏州215006 [2]苏州城市学院,苏州215104 [3]吉林大学符号计算与知识工程教育部重点实验室,长春130012

出　　处：《数据采集与处理》2023年第2期350-363,共14页Journal of Data Acquisition and Processing

基　　金：符号计算与知识工程教育部重点实验室(吉林大学)开放课题(93K172021K08);江苏高校优势学科建设工程(PAPD)。

摘　　要：低分辨率是影响人脸识别精度的重要因素。一种有效方法是使用图像超分辨率技术对低分辨率图像重建,生成超分辨率图像后再对其作人脸识别,从而克服低分辨率面部图像对人脸识别的限制。但是,现有超分辨率方法在重建过程中往往忽略了保持其原始身份信息,这直接影响生成图像的人脸识别结果。针对上述问题,提出了一种身份保持约束下的面部超分辨率重建方法IPNet,在提高低分辨率面部图像质量的同时,能保持重建后的面部图像身份。IPNet方法将语义分割网络和面部生成器相结合,通过语义分割网络提取低维隐码和多分辨率空间特征,进而指导面部生成器输出接近于原图的真实面部图像。在此基础上引入人脸识别网络,将身份信息整合到超分辨率方法中,从而约束重建前后的面部图像身份保持一致。实验结果表明,IPNet方法在超分辨率图像质量和身份保持上均优于其他对比方法。Low resolution is an important factor that affects the accuracy of face recognition.To overcome the limitation of low-resolution facial images on face recognition,one effective solution is adopting super-resolution methods to reconstruct low-resolution images and then identify the generated facial images.However,existing super-resolution methods typically fail to consider facial identity preservation during reconstruction,which directly results in poor face recognition performance of reconstructed images.To address the issue mentioned above,this paper proposes a face super-resolution reconstruction method with identity preserving,called IPNet.This method can simultaneously improve the quality of low-resolution facial images and preserve the identity of reconstructed images.IPNet consists of a semantic segmentation network and a face generator.The semantic segmentation network is introduced to extract low-dimensional latent code and multi-resolution spatial features.Then,the extracted features guide the face generator to output super-resolution images similar to the authentic images.Furthermore,we introduce the face recognition network to integrate the face identity information into the super-resolution model,thus maintaining the identity of reconstructed facial images consistent with original images.Experimental results show that IPNet achieves better results than other comparison methods in terms of both super-resolution image quality and identity preservation,demonstrating effectiveness of the proposed method.

关 键 词：超分辨率 人脸识别 语义分割 面部生成器 身份保持 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]