# Face Aging With Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1702.01983 · HTML: https://mlsanity.com/paper/1702.01983/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2017.

## Built on by (most cited first, 30 of 51)

- [Only a Matter of Style: Age Transformation Using a Style-Based Regression Model](https://mlsanity.com/paper/2102.02754/) (2021)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation](https://mlsanity.com/paper/2107.02408/) (2021)
- [DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models](https://mlsanity.com/paper/2302.06826/) (2023)
- [On the use of automatically generated synthetic image datasets for benchmarking face recognition](https://mlsanity.com/paper/2106.04215/) (2021)
- [Wavelet-Packets for Deepfake Image Analysis and Detection](https://mlsanity.com/paper/2106.09369/) (2021)
- [Face Aging via Diffusion-based Editing](https://mlsanity.com/paper/2309.11321/) (2023)
- [Custom Structure Preservation in Face Aging](https://mlsanity.com/paper/2207.11025/) (2022)
- [Pluralistic Aging Diffusion Autoencoder](https://mlsanity.com/paper/2303.11086/) (2023)
- [A Study of the Human Perception of Synthetic Faces](https://mlsanity.com/paper/2111.04230/) (2021)
- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [Image-to-Video Generation via 3D Facial Dynamics](https://mlsanity.com/paper/2105.14678/) (2021)
- [Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.07789/) (2021)
- [Disentangled Lifespan Face Synthesis](https://mlsanity.com/paper/2108.02874/) (2021)
- [Continuous Face Aging via Self-estimated Residual Age Embedding](https://mlsanity.com/paper/2105.00020/) (2021)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [High-Fidelity Eye Animatable Neural Radiance Fields for Human Face](https://mlsanity.com/paper/2308.00773/) (2023)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removal](https://mlsanity.com/paper/2104.09457/) (2021)
- [Wasserstein Geodesic Generator for Conditional Distributions](https://mlsanity.com/paper/2308.10145/) (2023)
- [DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training](https://mlsanity.com/paper/2101.07524/) (2021)
- [Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning](https://mlsanity.com/paper/2102.12139/) (2021)
- [K-FACE: A Large-Scale KIST Face Database in Consideration with Unconstrained Environments](https://mlsanity.com/paper/2103.02211/) (2021)
- [Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model](https://mlsanity.com/paper/2307.11980/) (2023)
- [Video Face Re-Aging: Toward Temporally Consistent Face Re-Aging](https://mlsanity.com/paper/2311.11642/) (2023)
- [Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric](https://mlsanity.com/paper/2401.14036/) (2024)
- [GANs Conditioning Methods: A Survey](https://mlsanity.com/paper/2408.15640/) (2024)
- [AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models](https://mlsanity.com/paper/2510.05715/) (2025)
- [Rayleigh EigenDirections (REDs): GAN latent space traversals for multidimensional features](https://mlsanity.com/paper/2201.10423/) (2022)
- [Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data](https://mlsanity.com/paper/2208.13146/) (2022)

## Abstract (arXiv)

It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the GAN-based method for automatic face aging. Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person's identity in the aged version of his/her face. To this end, we introduce a novel approach for "Identity-Preserving" optimization of GAN's latent vectors. The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.