面向非配合场景的人脸重建及识别方法  

Face reconstruction and recognition in non-cooperative scenes

作　　者：林乐平[1,2] 卢增通 欧阳宁[1,2] Le-ping LIN;Zeng-tong LU;Ning OUYANG(Ministry of Education Key Laboratory of Cognitive Radio and Information Processing,Guilin University of Electronic Technology,Guilin 541004,China;School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学认知无线电与信息处理省部共建教育部重点实验室,广西桂林541004 [2]桂林电子科技大学信息与通信学院,广西桂林541004

出　　处：《吉林大学学报（工学版）》2022年第12期2941-2946,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(62001133,61661017);广西科技重大专项项目(桂科AA20302001);广西科技基地和人才专项项目(桂科AD19110060);广西无线宽带通信与信号处理重点实验室基金项目(GXKL06200114).

摘　　要：针对非配合场景下,姿态偏差及低分辨率条件下人脸重建效果差、识别率低等问题,提出一种三元对抗重建识别网络。该方法先通过编解码网络将人脸图像重建为高分辨率正脸图像,再对重建人脸进行识别。在网络训练上,设计了一种最短距离三元组损失函数,并将其与对抗机制结合,使网络对同一个人提取的特征相似性高,而与其他人提取出的特征相似度低。实验结果表明,在大角度低分辨率条件下,本文网络性能优于目前领先的人脸姿态矫正算法。In non-cooperative scenes,to solve the problems of the poor reconstruction of faces and the low accuracy of face recognition due to the bad condition of posture deviations and the low image resolution,a triplet loss constrained generative adversarial network for reconstruction and recognition was proposed.The input faces were reconstructed firstly by encoder-decoder network,and then the reconstructed faces were recognized.In terms of training,a shortest distance triplet loss function was designed jointly with the adversarial mechanism,which increases the similarity intra-identification while enlarge the discrepancy inter-identification on the extracted feature representations.It is shown by the experiments that even under the condition of large angle deviations and low image resolution,the proposed algorithm performs better than currently leading face pose correction algorithms.

关 键 词：信号与信息处理 人脸姿态矫正 最短距离三元组损失 生成对抗网络 编解码网络 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]