Abstract
Most existing bottleneck-based Generative Adversarial Networks suffer from ghosting artifacts or blur for generating ageing results with an increased age gap. Although this can be solved using data collected over long age spans, it is challenging and tedious. This work proposes a multi-scale target age-based style face ageing model using an encoder-decoder architecture to generate high-fidelity face images under ageing. Further, we propose using skip connections with selective transfer units (STU) in the encoder-decoder architecture to adaptively select and modify the encoder feature to enhance face ageing results. Unlike conditional GAN (cGAN) approaches that rely on age as a condition to train the generator, we used style information gathered from a random image of the target age group to train the generator. The qualitative and quantitative results on public datasets show that our model can generate photo-realistic synthetic face images of the target age group. The proposed model can also generate diverse age groups for age progression and regression tasks. Furthermore, the analysis using six different Face Recognition Systems (FRS) also indicates the ability of the proposed approach to preserve the identity resulting in \(89.82\%\) False Non Match Rate (FNMR) at False Match Rate (FMR) of \(0.0001\%\).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017)
Boutros, F., Damer, N., Kirchbuchner, F., Kuijper, A.: ElasticFace: elastic margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1578–1587 (2022)
Chandaliya, P.K., Nain, N.: ChildGAN: face aging and rejuvenation to find missing children. Pattern Recogn. 129, 108761 (2022)
Chandaliya, P.K., Sinha, A., Nain, N.: ChildFace: gender aware child face aging. In: BIOSIG 2020, pp. 255–263 (2020)
Chen, B.-C., Chen, C.-S., Hsu, W.H.: Cross-age reference coding for age-invariant face recognition and retrieval. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 768–783. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_49
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style (2015)
Georgopoulos, M., Oldfield, J., Nicolaou, M.A., Panagakis, Y., Pantic, M.: Enhancing facial data diversity with style-based face aging (2020)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)
Grimmer, M., Ramachandra, R., Busch, C.: Deep face age progression: a survey. IEEE Access 9, 83376–83393 (2021). https://doi.org/10.1109/ACCESS.2021.3085835
Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Proceedings of the 6th International Conference on Neural Information Processing Systems, NIPS 1993, pp. 3–10. Morgan Kaufmann Publishers Inc. (1993)
Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks (2018)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR, pp. 4396–4405 (2019)
Kemelmacher-Shlizerman, I., Suwajanakorn, S., Seitz, S.M.: Illumination-aware age progression. In: CVPR, pp. 3334–3341 (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2016)
Lanitis, A., Taylor, C., Cootes, T.: Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002). https://doi.org/10.1109/34.993553
Li, Y., Wang, N., Liu, J., Hou, X.: Demystifying neural style transfer. In: AAAI, pp. 2230–2236 (2017)
Liu, M., et al.: STGAN: a unified selective transfer network for arbitrary image attribute editing. In: CVPR (2019)
Liu, M.Y., et al.: Few-shot unsupervised image-to-image translation. In: CVPR, pp. 1–8 (2019)
Mirza, M., Osindero, S.: Conditional generative adversarial nets (2014). http://arxiv.org/abs/1411.1784
Praveen, K.C., Neeta, N.: Conditional perceptual adversarial variational autoencoder for age progression and regression on child face. In: ICB, pp. 1–8 (2019)
Chandaliya, P.K., Kumar, V., Harjani, M., Nain, N.: SCDAE: ethnicity and gender alteration on CLF and UTKFace dataset. In: CVIP, pp. 294–306 (2019)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2016)
Ramanathan, N., Chellappa, R.: Modeling age progression in young faces. In: CVPR, pp. 387–394 (2006)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Rothe, R., Timofte, R., Van Gool, L.: Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vis. 126(2), 144–157 (2018)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs (2016)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR (2015)
Shi, Y., Jain, A.: Probabilistic face embeddings. In: ICCV, pp. 6901–6910 (2019)
Tazoe, Y., Gohara, H., Maejima, A., Morishima, S.: Facial aging simulator considering geometry and patch-tiled texture. In: ACM SIGGRAPH 2012 Posters, pp. 90:1–90:1 (2012)
Wang, Z., Tang, X., Luo, W., Gao, S.: Face aging with identity-preserved conditional generative adversarial networks. In: CVPR (2018)
Yang, H., Huang, D., Wang, Y., Jain, A.K.: Learning face age progression: a pyramid architecture of GANs. In: CVPR, pp. 31–39. IEEE Computer Society (2018)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
Zhang, K., Su, Y., Guo, X., Qi, L., Zhao, Z.: Mu-GAN: facial attribute editing based on multi-attention mechanism, vol. 8, p. 1614 (2021). https://doi.org/10.1109/JAS.2020.1003390
Zhang, Z., Song, Y., Qi, H.: Age progression/regression by conditional adversarial autoencoder. In: CVPR, pp. 4352–4360. IEEE Computer Society (2017)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp. 2242–2251 (2017)
Acknowledgement
This work was supported by the European Union’s Horizon 2020 Research and Innovation Program under Grant 883356.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chandaliya, P.K., Raja, K., Rajendra, G.S., Nain, N., Ramachandra, R., Busch, C. (2024). MuSTAT: Face Ageing Using Multi-scale Target Age Style Transfer. In: Kaur, H., Jakhetiya, V., Goyal, P., Khanna, P., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2023. Communications in Computer and Information Science, vol 2010. Springer, Cham. https://doi.org/10.1007/978-3-031-58174-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-58174-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-58173-1
Online ISBN: 978-3-031-58174-8
eBook Packages: Computer ScienceComputer Science (R0)