# Global and Local Consistent Age Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1801.08390 · HTML: https://mlsanity.com/paper/1801.08390/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

## Built on by (most cited first, 12 of 12)

- [Collaging Class-specific GANs for Semantic Image Synthesis](https://mlsanity.com/paper/2110.04281/) (2021)
- [Pluralistic Aging Diffusion Autoencoder](https://mlsanity.com/paper/2303.11086/) (2023)
- [SIEDOB: Semantic Image Editing by Disentangling Object and Background](https://mlsanity.com/paper/2303.13062/) (2023)
- [Continuous Face Aging via Self-estimated Residual Age Embedding](https://mlsanity.com/paper/2105.00020/) (2021)
- [Local and Global GANs with Semantic-Aware Upsampling for Image Generation](https://mlsanity.com/paper/2203.00047/) (2022)
- [DiffAge3D: Diffusion-based 3D-aware Face Aging](https://mlsanity.com/paper/2408.15922/) (2024)
- [AgeFlow: Conditional Age Progression and Regression with Normalizing Flows](https://mlsanity.com/paper/2105.07239/) (2021)
- [Exploring 3D-aware Lifespan Face Aging via Disentangled Shape-Texture Representations](https://mlsanity.com/paper/2312.16881/) (2023)
- [RoutingGAN: Routing Age Progression and Regression with Disentangled Learning](https://mlsanity.com/paper/2102.00601/) (2021)
- [A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks](https://mlsanity.com/paper/2109.07373/) (2021)
- [Task-Decoupled Image Inpainting Framework for Class-specific Object Remover](https://mlsanity.com/paper/2410.02894/) (2024)
- [DiverAge: Reliable Pluralistic Face Aging with Cross-Age Identity Relation Guidance](https://mlsanity.com/paper/2606.04881/) (2026)

## Abstract (arXiv)

Age progression/regression is a challenging task due to the complicated and non-linear transformation in human aging process. Many researches have shown that both global and local facial features are essential for face representation, but previous GAN based methods mainly focused on the global feature in age synthesis. To utilize both global and local facial information, we propose a Global and Local Consistent Age Generative Adversarial Network (GLCA-GAN). In our generator, a global network learns the whole facial structure and simulates the aging trend of the whole face, while three crucial facial patches are progressed or regressed by three local networks aiming at imitating subtle changes of crucial facial subregions. To preserve most of the details in age-attribute-irrelevant areas, our generator learns the residual face. Moreover, we employ an identity preserving loss to better preserve the identity information, as well as age preserving loss to enhance the accuracy of age synthesis. A pixel loss is also adopted to preserve detailed facial information of the input face. Our proposed method is evaluated on three face aging datasets, i.e., CACD dataset, Morph dataset and FG-NET dataset. Experimental results show appealing performance of the proposed method by comparing with the state-of-the-art.