# Learning Face Age Progression: A Pyramid Architecture of GANs

arXiv: https://arxiv.org/abs/1711.10352 · HTML: https://mlsanity.com/paper/1711.10352/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2017.

## Built on by (most cited first, 26 of 26)

- [Talk-to-Edit: Fine-Grained Facial Editing via Dialog](https://mlsanity.com/paper/2109.04425/) (2021)
- [Only a Matter of Style: Age Transformation Using a Style-Based Regression Model](https://mlsanity.com/paper/2102.02754/) (2021)
- [Point, Segment and Count: A Generalized Framework for Object Counting](https://mlsanity.com/paper/2311.12386/) (2023)
- [Detecting and Recovering Sequential DeepFake Manipulation](https://mlsanity.com/paper/2207.02204/) (2022)
- [When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework](https://mlsanity.com/paper/2103.01520/) (2021)
- [Custom Structure Preservation in Face Aging](https://mlsanity.com/paper/2207.11025/) (2022)
- [Multi-Scale Adaptive Network for Single Image Denoising](https://mlsanity.com/paper/2203.04313/) (2022)
- [Disentangled Lifespan Face Synthesis](https://mlsanity.com/paper/2108.02874/) (2021)
- [Identity-Preserving Aging of Face Images via Latent Diffusion Models](https://mlsanity.com/paper/2307.08585/) (2023)
- [Continuous Face Aging via Self-estimated Residual Age Embedding](https://mlsanity.com/paper/2105.00020/) (2021)
- [When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark](https://mlsanity.com/paper/2210.09835/) (2022)
- [Robust Sequential DeepFake Detection](https://mlsanity.com/paper/2309.14991/) (2023)
- [Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric](https://mlsanity.com/paper/2401.14036/) (2024)
- [Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing](https://mlsanity.com/paper/2411.15702/) (2024)
- [AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models](https://mlsanity.com/paper/2510.05715/) (2025)
- [AgeFlow: Conditional Age Progression and Regression with Normalizing Flows](https://mlsanity.com/paper/2105.07239/) (2021)
- [Evaluating the Efficacy of Skincare Product: A Realistic Short-Term Facial Pore Simulation](https://mlsanity.com/paper/2302.11950/) (2023)
- [Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map](https://mlsanity.com/paper/2308.13245/) (2023)
- [GroupDiff: Diffusion-based Group Portrait Editing](https://mlsanity.com/paper/2409.14379/) (2024)
- [RoutingGAN: Routing Age Progression and Regression with Disentangled Learning](https://mlsanity.com/paper/2102.00601/) (2021)
- [Can GAN-induced Attribute Manipulations Impact Face Recognition?](https://mlsanity.com/paper/2209.02941/) (2022)
- [Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques](https://mlsanity.com/paper/2302.11704/) (2023)
- [Mitigating the Impact of Attribute Editing on Face Recognition](https://mlsanity.com/paper/2403.08092/) (2024)
- [Identity Preserving Latent Diffusion for Brain Aging Modeling](https://mlsanity.com/paper/2503.09634/) (2025)
- [Face Age Progression With Attribute Manipulation](https://mlsanity.com/paper/2106.07696/) (2021)
- [Inclusive Review on Advances in Masked Human Face Recognition Technologies](https://mlsanity.com/paper/2508.00841/) (2025)

## Abstract (arXiv)

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to generate more lifelike facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer manner. The proposed method is applicable to diverse face samples in the presence of variations in pose, expression, makeup, etc., and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.