Face Age Editing Methods Based on Generative Adversarial Network: A Survey

doi:10.11871/jfdc.issn.2096-742X.2023.02.001

Abstract

Abstract:

[Objective] This paper provides a comprehensive and systematic review of the face age editing methods based on generative adversarial network. [Coverage] This survey contains numerous studies including publications in top-tier international journals and conferences, as well as other representative works in this field. [Methods] From the perspective of the internal mechanism of generative adversarial networks, these studies are categorized according to how the latent vector is obtained and manipulated, and a brief analysis and comparison of the similarity and difference of specific models is provided. [Results] With the rapid development of generative adversarial networks, the performance of the face age editing method in all aspects has been continuously and steadily improved, while there are still challenges and problems in various aspects. [Limitations] Due to the lack of a developed and mature evaluation standard, it is temporarily impossible for this paper to objectively measure and compare the discussed methods with a set of unified quantitative metrics. [Conclusions] Although significant outcome has been achieved, existing methods still face unsolved problems, including the flexibility of the editing process, the adaptability to extreme editing cases, and the diversity of generation results.

Key words: deep learning, generative adversarial network, image translation, facial attribute, face age editing

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.

Figures/Tables 14

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Table 1

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Table 2

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Table 3

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Table 4

Summary of latent space interpolation based face age editing methods"

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^[34]	$W, ? W +$	聚类中心的差向量	FFHQ	FID, US
StyleSpaceAnalysis^[35]	$S$	聚类中心的差向量	FFHQ	ACA, DCI
ACU^[90]	$S$	聚类中心的差向量	FFHQ	ACA, FID
InterFaceGAN^[33]	$Z, ? W$	支持向量机分类平面法向量	CelebA-HQ	-
EnjoyEditingGAN^[37]	$Z, ? W$	多层感知机预测	FFHQ, CelebA, CelebA-HQ	FVA, US
Latent-Transformer^[36]	$W +$	Transformer预测	CelebA-HQ	FVA, APR
Style-Transformer^[91]	$W +$	Transformer预测	FFHQ, CelebA-HQ	FID
SAM^[92]	$W +$	编码器预测	CelebA-HQ	DMEA, US

Table 4

Fig.9

Fig.10

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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^[40]	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^[41]	1690	515	15-68	年龄值	灰度	受控	受控环境下采集
MORPH Alb.2^[41]	55134	13618	16-77	年龄值	彩色	受控	受控环境下采集
CACD^[42]	163446	2000	16-62	年龄值	彩色	非受控	谷歌图像搜索引擎
IMDB-WIKI^[43]	523051	20284	0-100	年龄值	灰度、彩色	非受控	IMDB、Wikipedia网站
UTKFace^[44]	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^[45]	70000	-	0-70+	年龄组	彩色	非受控	原始FFHQ数据集

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^[56]	双域	单向	10	4	FG-Net, MORPH, CACD	MEA, FVC, FVA
AW-GAN^[57]	双域	单向	10	4	MORPH, CACD	MEA, DMEA, FVC, FVA, APR
A³GAN^[58]	双域	单向	10	4	FG-Net, MORPH, CACD, CelebA	MEA, DMEA, FVC, FVA, APR
PFA-GAN^[54]	多域	单向	10	4	FG-Net, MORPH, CACD	IS, MEA, DMEA, FVC, FVA, PCC
S²GAN^[59]	多域	单向	10	5	MORPH, CACD	ACA, FID, MEA, DMEA, FVA
IPC-GAN^[60]	多域	单向	10	5	CACD	IS, US
PA-GAN++^[61]	多域	单向	10	4	FG-Net, MORPH, CACD	MEA, FVC, FVA, US
ldGAN^[62]	多域	单向	3	12	MORPH	MEA, FVC
CAN-GAN^[63]	多域	单向	10	4	FG-Net, MORPH, CACD	MEA, DMEA, FVC, FVA
GLCA-GAN^[64]	多域	单向	10	4	FG-Net, MORPH, CACD	FVA
WaveletGLCA-GAN^[65]	多域	单向	10	4	FG-Net, MORPH, CACD	MEA, FVA
AM-GAN^[66]	多域	单向	连续年龄编辑		FFHQ-Aging	FID, DMAE
AA-GAN^[67]	多域	单向	10	5	MORPH	MEA, FVC, FVA
MTFE-GAN^[68]	多域	单向	10	4	MORPH, CACD	FID, MEA, FVA, KL散度
DEF-Net^[69]	多域	单向	10	4	MORPH, CACD	US
ranking-GAN^[70]	多域	单向	5	7	MORPH	MEA, FVC
Triple-GAN^[71]	多域	单向	10	5	MORPH, CACD	ACA, MEA, FVC, FVA
C-GAN^[72]	多域	单向	10	7	FG-Net, MORPH, CACD	FVA, US
Dual-cGAN^[75]	多域	循环	10	9	FG-Net, UTKFace	FVC, US
F-GAN^[76]	多域	循环	10	6	FG-Net, CelebA	DMEA, US
SA-GAN^[77]	多域	循环	4~30	9	FG-Net, MORPH, CACD, UTKFace, IMDB-WIKI, CelebA	DMEA, FVC, FVA
NSG-GAN^[78]	多域	循环	10	4	FG-Net, MORPH, CACD	DMEA, FVC, FVA

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^[44]	2	对抗训练	5~10	10	FG-Net, MORPH, CACD	US
AgeGAN++^[80]	2	对抗训练	4~30	9	FG-Net, MORPH, CACD	FID, EMA, FVC, US
AIM^[12]	2	域适应	老化/年轻化		FG-Net, MORPH, CACD	FVA
MLTFace^[13,14]	2	域适应	10	7	FG-Net, MORPH, CACD, AgeDB, LCAF	ACA, EVC, FVA, US
Age-cGAN^[81]	2	优化重建	10	6	IMDB-WIKI	FVA
CFA-GAN^[82]	2	正交分解	连续年龄编辑		MORPH	MEA, DMEA, FVC, FVA
CA-GAN^[83]	2	伪成对数据	4	8	FG-Net, CACD, ITWCC	FVA
PAT-GAN^[84]	2	特征调制	连续年龄编辑		CACD, FFHQ-Aging	FID, MEA, FVA
HRFAE^[85]	2	特征调制	连续年龄编辑		CACD, FFHQ-Aging, CelebA-HQ	MEA, APR, Blurriness
LATS^[45]	2	AdaIN	3~20	10	CACD, FFHQ-Aging, CelebA-HQ	US
ReAgingGAN^[86]	2	AdaIN	连续年龄编辑		FG-Net, MORPH, CACD	FID, ACA
DAAE^[87]	3	VAE优化目标	连续年龄编辑		FG-Net, MORPH, CACD, UTKFace, AgeDB	DMEA, MEA, FVA
AgeFlow^[88]	3	基于flow的模型	10	4	FG-Net, MORPH, CACD, CelebA	ACA, FVC, APR
DLATS^[89]	3	人脸形状正则化	10	6	FFHQ-Aging	FVA, US