Progressive Face Age Synthesis Algorithm Based on Generative Adversarial Network

doi:10.12068/j.issn.1005-3026.2024.07.005

Abstract

Abstract:

The goal of face age synthesize （FAS） is to synthesize face images of specified ages based on the source face image， while preserving personal characteristics and identity information of the face. To solve the problem that irrelevant features are easy to change and artifact ghosting occurs when age is changed， a progressive face age synthesis algorithm based on generative adversarial network is proposed. The age editing module based on gate recurrent unit is used to filter or add features adaptively， and attribute decoupling module is used for adversarial learning in the latent space. Through the adversarial strategy of generator and discriminator， the real and natural face synthesis is guaranteed. The age classification constraint is used to fit the specific age distribution. In order to preserve age?independent properties， reconstruction learning is also introduced into generative adversarial network. Experimental results on CACD dataset show that， compared with other algorithms based on conditional generative adversarial network， the proposed algorithm has reduced artifacts and distortions， enhanced age significance， and has better age accuracy and higher identity consistency.

Key words: face age synthesis, generative adversarial network, attribute decoupling, latent space, gate recurrent unit, reconstruction learning

CLC Number:

TP 391

Xiao-yu YANG, Ai-xia WANG, Gang YANG, Jing-jiao LI. Progressive Face Age Synthesis Algorithm Based on Generative Adversarial Network[J]. Journal of Northeastern University(Natural Science), 2024, 45(7): 944-952.

Figures/Tables 11

References 33

1	Suo J L， Zhu S C， Shan S G，et al.A compositional and dynamic model for face aging［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2010，32（3）：385-401.
2	Tazoe Y， Gohara H， Maejima A，et al.Facial aging simulator considering geometry and patch‑tiled texture［M］.New York：ACM，2012.
3	Tsai M H， Liao Y K， Lin I C.Human face aging with guided prediction and detail synthesis［J］.Multimedia Tools and Applications，2014，72（1）：801-824.
4	Todd J T， Mark L S， Shaw R E，et al.The perception of human growth［J］.Scientific American，1980，242（2）：132-145.
5	Suo J L， Chen X L， Shan S G，et al.A concatenational graph evolution aging model［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2012，34（11）：2083-2096.
6	Ramanathan N， Chellappa R.Modeling age progression in young faces［C］//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York，2006：387-394.
7	Kemelmacher‑Shlizerman I， Suwajanakorn S， Seitz S M.Illumination‑aware age progression［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Columnus，2014：3334-3341.
8	Tiddeman B， Burt M， Perrett D.Prototyping and transforming facial textures for perception research［J］.IEEE Computer Graphics and Applications，2001，21（5）：42-50.
9	Wang W， Cui Z， Yan Y，et al.Recurrent face aging［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas，2016：2378–2386.
10	Duong C N， Luu K， Quach K G，et al.Longitudinal face modeling via temporal deep restricted boltzmann machines［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas，2016：5772-5780.
11	Duong C N， Quac K G， Luu K，et al.Temporal non‑volume preserving approach to facial age‑progression and age‑invariant face recognition［C］//Proceedings of the IEEE International Conference on Computer Vision.Venice，2017：3735-3743.
12	Goodfellow I， Pouget‑Abadie J， Mirza M，et al.Generative adversarial nets［J］.Advances in Neural Information Processing Systems，2014，27（2）：2672-2680.
13	Zhu J Y， Park T， Isola P，et al.Unpaired image‑to‑image translation using cycle consistent adversarial networks［C］//Proceedings of the IEEE International Conference on Computer Vision.Venice，2017：2223-2232.
14	Isola P， Zhu J Y， Zhou T H，et al.Image‑to‑image translation with conditional adversarial networks［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu，2017：1125-1134.
15	Choi Y， Uh Y， Yoo J，et al.Stargan v2：diverse image synthesis for multiple domains［C］//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Seattle，2020：8188-8197.
16	Park T， Liu M Y， Wang T C，et al.GauGAN：semantic image synthesis with spatially adaptive normalization［M］.New York：ACM，2019.
17	Karras T， Aittala M， Laine S，et al.Alias‑free generative adversarial networks［J］.Advances in Neural Information Processing Systems，2021，34（1）：852-863.
18	Mirza M， Osindero S.Conditional generative adversarial nets ［EB/OL］.（2014-10-06）［2023-03-04］..
19	Antipov G， Baccouche M， Dugelay J L.Face aging with conditional generative adversarial networks［C］//2017 IEEE International Conference on Image Processing.Beijing，2017：2089-2093.
20	Zhang Z F， Song Y， Qi H R.Age progression/regression by conditional adversarial autoencoder［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu，2017：5810-5818.
21	Wang Z W， Tang X， Luo W X，et al.Face aging with identity‑preserved conditional generative adversarial networks［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City，2018：7939-7947.
22	Yang H Y， Huang D， Wang Y H，et al.Learning face age progression：a pyramid architecture of gans［C］//IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City，2018：31-39.
23	Yang H Y， Huang D， Wang Y H，et al.Learning continuous face age progression：a pyramid of gans［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2021，43（2）：499-515.
24	Liu Y F， Li Q， Sun Z N.Attribute‑aware face aging with wavelet‑based generative adversarial networks［C］//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach，2019：11877-11886.
25	Shao J， Bui T D.Wavelet‑based multi‑level generative adversarial networks for face aging［C］//The 32nd British Machine Vision Conference.Virtual：British Machine Vision Association，2021：1388-1399.
26	Li P P， Hu Y B， Li Q，et al.Global and local consistent age generative adversarial networks［C］//International Conference on Pattern Recognition.Beijing，2018：1073-1078.
27	Huang Z Z， Chen S Z， Zhang J P，et al.PFA‑GAN：progressive face aging with generative adversarial network［J］.IEEE Transactions on Information Forensics and Security，2020，15（16）：2031-2045.
28	Li Q， Liu Y F， Sun Z N.Age progression and regression with spatial attention modules［C］//Proceedings of the AAAI Conference on Artificial Intelligence.New York：AAAI，2020：11378-11385.
29	Huang Z Z， Zhang J P， Shan H M.When age‑invariant face recognition meets face age synthesis：a multi‑task learning framework［C］//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Virtual，2021：7282-7291.
30	Lample G， Zeghidour N， Usunier N，et al.Fader networks：manipulating images by sliding attributes［C］//Annual Conference on Neural Information Processing Systems.Long Beach：MIT Press，2017：5963-5972.
31	Arjovsky M， Chintala S， Bottou L.Wasserstein GAN ［EB/OL］.（2017-01-26）［2023-03-04］..
32	Gulrajani I， Ahmed F， Arjovsky M，et al.Improved training of Wasserstein gans［C］//Annual Conference on Neural Information Processing Systems.Long Beach：MIT Press，2017：5769-5779.
33	Chen B C， Chen C S， Hsu W H.Face recognition and retrieval using cross‑age reference coding with cross‑age celebrity dataset［J］.IEEE Transactions on Multimedia，2015，17（6）：804-815.

年龄组	21~30	31~40	41~50	51~62
训练集	38 620	41 024	37 805	26 534
测试集	2 085	2 111	1 955	1 465

年龄组	21~30	31~40	41~50	51~62
训练集	38 620	41 024	37 805	26 534
测试集	2 085	2 111	1 955	1 465

年龄组	21~30	31~40	41~50	51~62
真实人脸	28.43±7.28	35.31±8.83	43.00±11.25	49.88±12.20
合成人脸	29.63±7.94	36.34±9.42	44.43±9.87	48.39±11.95

年龄组	21~30	31~40	41~50	51~62
真实人脸	28.43±7.28	35.31±8.83	43.00±11.25	49.88±12.20
合成人脸	29.63±7.94	36.34±9.42	44.43±9.87	48.39±11.95

年龄组	21~30	31~40	41~50	51~62
IPCGAN	0.74	1.16	1.55	3.09
SAM-GAN	0.67	1.29	1.46	2.06
PFA-GAN	0.81	1.05	1.51	2.73
本文算法	1.20	1.03	1.43	1.49