数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (2): 2-23.
CSTR: 32002.14.jfdc.CN10-1649/TP.2023.02.001
doi: 10.11871/jfdc.issn.2096-742X.2023.02.001
• 专刊:“人工智能&大数据”科研范式变革专刊(上) • 上一篇 下一篇
刘云帆1,2(),李琦2,孙哲南1,2,*(),谭铁牛1,2
收稿日期:
2023-01-28
出版日期:
2023-04-20
发布日期:
2023-04-24
通讯作者:
孙哲南
作者简介:
刘云帆,中国科学院大学人工智能学院,博士研究生,主要研究方向为生物特征识别、计算机视觉、人脸图像编辑。基金资助:
LIU Yunfan1,2(),LI Qi2,SUN Zhenan1,2,*(),TAN Tieniu1,2
Received:
2023-01-28
Online:
2023-04-20
Published:
2023-04-24
Contact:
SUN Zhenan
摘要:
【目的】对基于生成对抗网络的人脸年龄编辑方法进行系统梳理和全面总结。【文献范围】本文从该领域发表在国内外一流会议期刊上的文章出发,按照引用关系对具有代表性的方法进行了检索,并对最终得到的众多研究工作进行了综述。【方法】本文从生成对抗网络内在机制的角度出发,按照隐向量求取和操控方式的不同,对现有方法进行了分类,并对各个模型之间的共性和差异进行了分析和比较。【结果】随着生成对抗网络的快速发展,人脸年龄编辑方法的各方面性能稳步提升,但是仍然面临着诸多问题和挑战。【局限】由于尚不存在一套通用的评价标准,因此本文暂时无法将所有涉及到的方法用一组统一的定量指标进行衡量和比较。【结论】虽然已经取得了显著的成果,但是现有方法在编辑过程的灵活性、极端情况的适应性以及输出结果的多样性方面还有亟待解决的问题。
刘云帆,李琦,孙哲南,谭铁牛. 基于生成对抗网络的人脸年龄编辑方法综述[J]. 数据与计算发展前沿, 2023, 5(2): 2-23.
LIU Yunfan,LI Qi,SUN Zhenan,TAN Tieniu. Face Age Editing Methods Based on Generative Adversarial Network: A Survey[J]. Frontiers of Data and Computing, 2023, 5(2): 2-23, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.02.001.
表1
常用公开人脸年龄编辑数据集"
数据集名称 | 图像数量 | 人物 数量 | 年龄 范围 | 标签 类型 | 图像属性 | 采集环境 | 图像来源 |
---|---|---|---|---|---|---|---|
FG-Net[ | 1002 | 82 | 0-69 | 年龄值 | 灰度、彩色 | 非受控 | 私人数码/扫描照片 |
MORPH Alb.1[ | 1690 | 515 | 15-68 | 年龄值 | 灰度 | 受控 | 受控环境下采集 |
MORPH Alb.2[ | 55134 | 13618 | 16-77 | 年龄值 | 彩色 | 受控 | 受控环境下采集 |
CACD[ | 163446 | 2000 | 16-62 | 年龄值 | 彩色 | 非受控 | 谷歌图像搜索引擎 |
IMDB-WIKI[ | 523051 | 20284 | 0-100 | 年龄值 | 灰度、彩色 | 非受控 | IMDB、Wikipedia网站 |
UTKFace[ | 20000 | - | 0-116 | 年龄值 | 灰度、彩色 | 非受控 | 谷歌、必应图像搜索引擎 |
FFHQ-Aging[ | 70000 | - | 0-70+ | 年龄组 | 彩色 | 非受控 | 原始FFHQ数据集 |
表2
基于图像域间映射的人脸年龄编辑方法总结"
模型名称 | 映射类型 | 网络类型 | 年龄组 跨度 | 年龄组 个数 | 人脸年龄编辑数据集 | 定量测量指标 |
---|---|---|---|---|---|---|
PA-GAN[ | 双域 | 单向 | 10 | 4 | FG-Net, MORPH, CACD | MEA, FVC, FVA |
AW-GAN[ | 双域 | 单向 | 10 | 4 | MORPH, CACD | MEA, DMEA, FVC, FVA, APR |
A3GAN[ | 双域 | 单向 | 10 | 4 | FG-Net, MORPH, CACD, CelebA | MEA, DMEA, FVC, FVA, APR |
PFA-GAN[ | 多域 | 单向 | 10 | 4 | FG-Net, MORPH, CACD | IS, MEA, DMEA, FVC, FVA, PCC |
S2GAN[ | 多域 | 单向 | 10 | 5 | MORPH, CACD | ACA, FID, MEA, DMEA, FVA |
IPC-GAN[ | 多域 | 单向 | 10 | 5 | CACD | IS, US |
PA-GAN++[ | 多域 | 单向 | 10 | 4 | FG-Net, MORPH, CACD | MEA, FVC, FVA, US |
ldGAN[ | 多域 | 单向 | 3 | 12 | MORPH | MEA, FVC |
CAN-GAN[ | 多域 | 单向 | 10 | 4 | FG-Net, MORPH, CACD | MEA, DMEA, FVC, FVA |
GLCA-GAN[ | 多域 | 单向 | 10 | 4 | FG-Net, MORPH, CACD | FVA |
WaveletGLCA-GAN[ | 多域 | 单向 | 10 | 4 | FG-Net, MORPH, CACD | MEA, FVA |
AM-GAN[ | 多域 | 单向 | 连续年龄编辑 | FFHQ-Aging | FID, DMAE | |
AA-GAN[ | 多域 | 单向 | 10 | 5 | MORPH | MEA, FVC, FVA |
MTFE-GAN[ | 多域 | 单向 | 10 | 4 | MORPH, CACD | FID, MEA, FVA, KL散度 |
DEF-Net[ | 多域 | 单向 | 10 | 4 | MORPH, CACD | US |
ranking-GAN[ | 多域 | 单向 | 5 | 7 | MORPH | MEA, FVC |
Triple-GAN[ | 多域 | 单向 | 10 | 5 | MORPH, CACD | ACA, MEA, FVC, FVA |
C-GAN[ | 多域 | 单向 | 10 | 7 | FG-Net, MORPH, CACD | FVA, US |
Dual-cGAN[ | 多域 | 循环 | 10 | 9 | FG-Net, UTKFace | FVC, US |
F-GAN[ | 多域 | 循环 | 10 | 6 | FG-Net, CelebA | DMEA, US |
SA-GAN[ | 多域 | 循环 | 4~30 | 9 | FG-Net, MORPH, CACD, UTKFace, IMDB-WIKI, CelebA | DMEA, FVC, FVA |
NSG-GAN[ | 多域 | 循环 | 10 | 4 | FG-Net, MORPH, CACD | DMEA, FVC, FVA |
表3
基于年龄特征分解的人脸年龄编辑方法总结"
模型名称 | 子空间数量 | 解耦方法 | 年龄组 跨度 | 年龄组 数量 | 人脸年龄编辑数据集 | 定量测量指标 |
---|---|---|---|---|---|---|
CAAE[ | 2 | 对抗训练 | 5~10 | 10 | FG-Net, MORPH, CACD | US |
AgeGAN++[ | 2 | 对抗训练 | 4~30 | 9 | FG-Net, MORPH, CACD | FID, EMA, FVC, US |
AIM[ | 2 | 域适应 | 老化/年轻化 | FG-Net, MORPH, CACD | FVA | |
MLTFace[ | 2 | 域适应 | 10 | 7 | FG-Net, MORPH, CACD, AgeDB, LCAF | ACA, EVC, FVA, US |
Age-cGAN[ | 2 | 优化重建 | 10 | 6 | IMDB-WIKI | FVA |
CFA-GAN[ | 2 | 正交分解 | 连续年龄编辑 | MORPH | MEA, DMEA, FVC, FVA | |
CA-GAN[ | 2 | 伪成对数据 | 4 | 8 | FG-Net, CACD, ITWCC | FVA |
PAT-GAN[ | 2 | 特征调制 | 连续年龄编辑 | CACD, FFHQ-Aging | FID, MEA, FVA | |
HRFAE[ | 2 | 特征调制 | 连续年龄编辑 | CACD, FFHQ-Aging, CelebA-HQ | MEA, APR, Blurriness | |
LATS[ | 2 | AdaIN | 3~20 | 10 | CACD, FFHQ-Aging, CelebA-HQ | US |
ReAgingGAN[ | 2 | AdaIN | 连续年龄编辑 | FG-Net, MORPH, CACD | FID, ACA | |
DAAE[ | 3 | VAE优化目标 | 连续年龄编辑 | FG-Net, MORPH, CACD, UTKFace, AgeDB | DMEA, MEA, FVA | |
AgeFlow[ | 3 | 基于flow的模型 | 10 | 4 | FG-Net, MORPH, CACD, CelebA | ACA, FVC, APR |
DLATS[ | 3 | 人脸形状正则化 | 10 | 6 | FFHQ-Aging | FVA, US |
表4
基于隐空间插值的人脸年龄编辑方法总结"
模型名称 | 插值隐空间 | 插值向量建模方法 | 人脸年龄编辑数据集 | 定量测量指标 |
---|---|---|---|---|
StyleGAN2Distillation[ | 聚类中心的差向量 | FFHQ | FID, US | |
StyleSpaceAnalysis[ | 聚类中心的差向量 | FFHQ | ACA, DCI | |
ACU[ | 聚类中心的差向量 | FFHQ | ACA, FID | |
InterFaceGAN[ | 支持向量机分类平面法向量 | CelebA-HQ | - | |
EnjoyEditingGAN[ | 多层感知机预测 | FFHQ, CelebA, CelebA-HQ | FVA, US | |
Latent-Transformer[ | Transformer预测 | CelebA-HQ | FVA, APR | |
Style-Transformer[ | Transformer预测 | FFHQ, CelebA-HQ | FID | |
SAM[ | 编码器预测 | CelebA-HQ | DMEA, US |
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