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Face aging with pixel-level alignment GAN

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Abstract

Face aging is of great significance in cross-time identity verification problem. However, there is still a huge gap between the synthesized face image and the real face in terms of quality and consistency due to identity ambiguity and image distortion caused by existing face aging methods. To meet this challenge, we propose a face aging framework named as Pixel-level Alignment GAN, PAGAN, to synthesize faces of different age groups. Face images are featured by age, identity, and fine-grained pixel-value to ensure the quality, which is a typical multi-task learning problem. The proposed face aging framework with PAGAN is a combination of age estimation, identity preservation, and image de-noising. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods not only in the accuracy of age classification but also in the image quality. With the proposed PAGAN, the face recognition accuracy with synthesized images has increased 0.21% and the image quality rating has increased around 5%, which proves the effectiveness and validity of proposed method.

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References

  1. Shu X, Xie G-S, Li Z, Tang J (2016) Age progression: Current technologies and applications. Neurocomputing 208:249–261. https://doi.org/10.1016/j.neucom.2016.01.101

    Article  Google Scholar 

  2. Shen Y, Gu J, Tang X, Zhou B (2020) Interpreting the latent space of gans for semantic face editing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 9243–9252

  3. Pei M, Wu X, Guo Y, Fujita H (2017) Small bowel motility assessment based on fully convolutional networks and long short-term memory. Knowl-Based Syst 121:163–172. https://doi.org/10.1016/j.knosys.2017.01.023

    Article  Google Scholar 

  4. Gao P, Zhang Q, Wang F, Xiao L, Fujita H, Zhang Y (2020) Learning reinforced attentional representation for end-to-end visual tracking. Inf Sci 517:52–67. https://doi.org/10.1016/j.ins.2019.12.084

    Article  Google Scholar 

  5. Wu X, Zhong M, Guo Y, Fujita H (2020) The assessment of small bowel motility with attentive deformable neural network. Inf Sci 508:22–32. https://doi.org/10.1016/j.ins.2019.08.059

    Article  Google Scholar 

  6. Liang H, Gao J, Qiang N (2021) A novel framework based on wavelet transform and principal component for face recognition under varying illumination. Appl Intell 51(3):1762–1783. https://doi.org/10.1007/s10489-020-01924-9

    Article  Google Scholar 

  7. Truong HP, Nguyen TP, Kim Y-G (2021) Weighted statistical binary patterns for facial feature representation. Appl Intell, pp 1–20. https://doi.org/10.1007/s10489-021-02477-1

  8. Zhang Z, Song Y, Qi H (2017) Age progression/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5810–5818

  9. Wang Z, Tang X, Luo W, Gao S (2018) Face aging with identity-preserved conditional generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7939–7947

  10. Liu Y, Li Q, Sun Z (2019) Attribute-aware face aging with wavelet-based generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11877–11886

  11. Ye L, Zhang B, Yang M, Lian W (2019) Triple-translation gan with multi-layer sparse representation for face image synthesis. Neurocomputing 358:294–308. https://doi.org/10.1016/j.neucom.2019.04.074

    Article  Google Scholar 

  12. Chandaliya PK, Nain N (2019) Conditional perceptual adversarial variational autoencoder for age progression and regression on child face. In: 2019 International conference on biometrics (ICB), IEEE, pp 1–8

  13. Zhao J, Xie X, Xu X, Sun S (2017) Multi-view learning overview: Recent progress and new challenges. Information Fusion 38:43–54. https://doi.org/10.1016/j.inffus.2017.02.007

    Article  Google Scholar 

  14. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  15. Cao W, Mirjalili V, Raschka S (2020) Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recogn Lett 140:325–331. https://doi.org/10.1016/j.patrec.2020.11.008

    Article  Google Scholar 

  16. Wang H, Yang Y, Liu B, Fujita H (2019) A study of graph-based system for multi-view clustering. Knowl-Based Syst 163:1009–1019. https://doi.org/10.1016/j.knosys.2018.10.022

    Article  Google Scholar 

  17. Chao G, Sun S, Bi J (2021) A survey on multi-view clustering. IEEE Transactions on Artificial Intelligence, https://doi.org/10.1109/TAI.2021.3065894

  18. Chao G, Sun J, Lu J, Wang A-L, Langleben DD, Li C-S, Bi J (2019) Multi-view cluster analysis with incomplete data to understand treatment effects. Information sciences 494:278–293. https://doi.org/10.1016/j.ins.2019.04.039

    Article  MathSciNet  Google Scholar 

  19. Chao G, Sun S (2019) Semi-supervised multi-view maximum entropy discrimination with expectation laplacian regularization. Information Fusion 45:296–306. https://doi.org/10.1016/j.inffus.2018.03.002

    Article  Google Scholar 

  20. Xiao Q, Dai J, Luo J, Fujita H (2019) Multi-view manifold regularized learning-based method for prioritizing candidate disease mirnas. Knowl-Based Syst 175:118–129. https://doi.org/10.1016/j.knosys.2019.03.023

    Article  Google Scholar 

  21. Zhang X, Yang Y, Li T, Zhang Y, Wang H, Fujita H (2021) Cmc: A consensus multi-view clustering model for predicting alzheimer?s disease progression. Comput Methods Prog Biomed 199:105895. https://doi.org/10.1016/j.cmpb.2020.105895

    Article  Google Scholar 

  22. Kuang P, Ma T, Chen Z, Li F (2019) Image super-resolution with densely connected convolutional networks. Appl Intell 49(1):125–136. https://doi.org/10.1007/s10489-018-1234-y

    Article  Google Scholar 

  23. Zhang F, Ma Y, Yuan G, Zhang H, Ren J (2021) Multiview image generation for vehicle reidentification. Appl Intell, pp 1–18. https://doi.org/10.1007/s10489-020-02171-8

  24. Zhu Z, Luo P, Wang X, Tang X (2014) Multi-view perceptron: a deep model for learning face identity and view representations. In: Advances in neural information processing systems, pp 217–225

  25. Kan M, Shan S, Chen X (2016) Multi-view deep network for cross-view classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4847–4855

  26. Chen Z-L, He Q-H, Pang W-F, Li Y-X (2018) Frontal face generation from multiple pose-variant faces with cgan in real-world surveillance scene. In: 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1308–1312

  27. Yu X, Porikli F, Fernando B, Hartley R (2020) Hallucinating unaligned face images by multiscale transformative discriminative networks. Int J Comput Vis 128 (2):500–526. https://doi.org/10.1007/s11263-019-01254-5

    Article  Google Scholar 

  28. Li L, Lin H-T (2006) Ordinal regression by extended binary classification. Advances in neural information processing systems 19:865–872

    Google Scholar 

  29. Niu Z, Zhou M, Wang L, Gao X, Hua G (2016) Ordinal regression with multiple output cnn for age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4920–4928

  30. Chen S, Zhang C, Dong M, Le J, Rao M (2017) Using ranking-cnn for age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5183–5192

  31. Polania L, Fung G, Wang D (2019) Ordinal regression using noisy pairwise comparisons for body mass index range estimation. In: 2019 IEEE Winter conference on applications of computer vision (WACV), IEEE, pp 782–790

  32. Hakura J, Kashiwakura M, Hiyama Y, Kurematsu M, Fujita H (2007) Facial expression recognition and synthesis toward construction of quasi-personality. WSEAS, AIKED07, paper Indexed as, pp 540–369

  33. Ramanathan N, Chellappa R (2008) Modeling shape and textural variations in aging faces. In: 2008 8th IEEE International conference on automatic face & gesture recognition, IEEE, pp 1–8

  34. Suo J, Chen X, Shan S, Gao W, Dai Q (2012) A concatenational graph evolution aging model. IEEE Trans Pattern Anal and Machine Intelligence 34(11):2083–2096. https://doi.org/10.1109/TPAMI.2012.22

    Article  Google Scholar 

  35. Tazoe Y, Gohara H, Maejima A, Morishima S (2012) Facial aging simulator considering geometry and patch-tiled texture. In: ACM SIGGRAPH 2012 Posters, pp 1–1

  36. Yang H, Huang D, Wang Y, Jain AK (2018) Learning face age progression: A pyramid architecture of gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 31–39

  37. Song J, Zhang J, Gao L, Liu X, Shen HT (2018) Dual conditional gans for face aging and rejuvenation.. In: IJCAI, pp 899–905

  38. Zhu H, Huang Z, Shan H, Zhang J (2020) Look globally, age locally: Face aging with an attention mechanism. In: ICASSP 2020-2020 IEEE International conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1963–1967

  39. Huang Z, Chen S, Zhang J, Shan H (2020) Pfa-gan: Progressive face aging with generative adversarial network. IEEE Trans Info Forensics and Security 16:2031–2045

    Article  Google Scholar 

  40. Du J-X, Zhai C-M, Ye Y-Q (2013) Face aging simulation and recognition based on nmf algorithm with sparseness constraints. Neurocomputing 116:250–259. https://doi.org/10.1016/j.neucom.2012.08.030

    Article  Google Scholar 

  41. He Z, Kan M, Shan S, Chen X (2019) S2gan: Share aging factors across ages and share aging trends among individuals. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 9440–9449

  42. Upchurch P, Gardner J, Pleiss G, Pless R, Snavely N, Bala K, Weinberger K (2017) Deep feature interpolation for image content changes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7064–7073

  43. Palsson S, Agustsson E, Timofte R, Van Gool L (2018) Generative adversarial style transfer networks for face aging. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 2084–2092

  44. Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232

  45. Mao X, Wang S, Zheng L, Huang Q (2018) Semantic invariant cross-domain image generation with generative adversarial networks. Neurocomputing 293:55–63. https://doi.org/10.1016/j.neucom.2018.02.092

    Article  Google Scholar 

  46. Choi Y, Choi M, Kim M, Ha J-W, Kim S, Choo J (2018) Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8789–8797

  47. Liu M, Ding Y, Xia M, Liu X, Ding E, Zuo W, Wen S (2019) Stgan: A unified selective transfer network for arbitrary image attribute editing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3673–3682

  48. Pumarola A, Agudo A, Martinez AM, Sanfeliu A, Moreno-Noguer F (2018) Ganimation: Anatomically-aware facial animation from a single image. In: Proceedings of the European conference on computer vision (ECCV), pp 818–833

  49. Chen Y-C, Shen X, Lin Z, Lu X, Pao I, Jia J, et al. (2019) Semantic component decomposition for face attribute manipulation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9859–9867

  50. Zeng J, Ma X, Zhou K (2019) Photo-realistic face age progression/regression using a single generative adversarial network. Neurocomputing 366:295–304. https://doi.org/10.1016/j.neucom.2019.07.085

    Article  Google Scholar 

  51. Li Q, Liu Y, Sun Z (2020) Age progression and regression with spatial attention modules. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 11378–11385

  52. Xiao T, Hong J, Ma J (2018) Elegant: Exchanging latent encodings with gan for transferring multiple face attributes. In: Proceedings of the european conference on computer vision (ECCV), pp 168–184

  53. Qian S, Lin K-Y, Wu W, Liu Y, Wang Q, Shen F, Qian C, He R (2019) Make a face: Towards arbitrary high fidelity face manipulation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10033–10042

  54. Or-El R, Sengupta S, Fried O, Shechtman E, Kemelmacher-Shlizerman I (2020) Lifespan age transformation synthesis. In: European conference on computer vision, Springer, pp 739–755

  55. Chen B-C, Chen C-S, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. In: European conference on computer vision, Springer, pp 768–783

  56. Terhorst P, Kolf JN, Damer N, Kirchbuchner F, Kuijper A (2020) Ser-fiq: Unsupervised estimation of face image quality based on stochastic embedding robustness. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5651–5660

  57. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 62172267), the National Key R&D Program of China (Grant No. 2019YFE0190500), the Natural Science Foundation of Shanghai, China (Grant No. 20ZR1420400), the State Key Program of National Natural Science Foundation of China (Grant No. 61936001).

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Xing Wu, Yafei Zhang, Qing Li, Yangyang Qi & Jianjia Wang

  2. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, 200444, China

    Xing Wu & Jianjia Wang

  3. Department of Computing, Imperial College London, London, 250329, United Kingdom

    Yike Guo

  4. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

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  1. Xing Wu
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Correspondence to Xing Wu.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning

Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

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Wu, X., Zhang, Y., Li, Q. et al. Face aging with pixel-level alignment GAN. Appl Intell 52, 14665–14678 (2022). https://doi.org/10.1007/s10489-022-03541-0

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