基于生成对抗网络的人脸年龄合成研究概述

Overview of Research on Face Age Synthesis Based on Generative Adversarial Networks

随着计算机视觉的蓬勃发展,人脸年龄合成相关方面工作吸引了一大批研究人员的注意。人脸中蕴藏着丰富的视觉信息,具有很大的可开发价值,在刑事侦查、出入境检查等方面均可以应用。传统的人脸老化方法在身份保持、计算开销、数据集要求等方面存在不足,生成对抗网络的快速发展使得人脸图像生成方面取得了显著的成果。本文首先概述了人脸年龄合成领域的相关发展背景,然后介绍了几种经典的用GAN进行人脸年龄合成的方法,最后概括了主观评价和客观评价这两种常用的年龄合成评价方法。希望可以对在该领域入门的人员有所帮助。

With vigorous development of computer vision,the work related to face age synthesis has attracted attention of a large number of researchers.Face contains rich visual information and has great exploitable value,which can be used in criminal investigations,entry and exit inspections,etc.Traditional face aging methods have deficiencies in identity maintenance,computational overhead,and data set requirements.Rapid development of the generative adversarial network has made remarkable achievements in face image generation.This article first outlines relevant development background in face age synthesis field,then introduces several classic methods of GAN for face synthesis,and finally summarizes subjective and objective evaluation of two commonly age synthesis evaluation methods,to help beginners in this field.