# Continuous Face Aging via Self-estimated Residual Age Embedding

**Authors:** Zeqi Li, Ruowei Jiang, Parham Aarabi  
**arXiv:** [2105.00020](https://arxiv.org/abs/2105.00020)  
**Date:** 2021-04-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2105.00020v1/

## Key points

- Significant advancements in face synthesis, particularly in face aging, using generative adversarial networks (GANs)
- Traditional approaches lack fine-grained control over continuous aging synthesis
- Authors Zeqi Li, Ruowei Jiang, and Parham Aarabi introduce a novel approach titled "Continuous Face Aging via Self-estimated Residual Age Embedding"
- Unified network structure integrates a linear age estimator into a GAN-based model for accurate age estimation and personalized target age embeddings
- Components include personalized residual age embedding of current age and exemplar-face aging basis of target age derived from learned weights within the linear age estimator framework
- Qualitative and quantitative evaluations demonstrate substantial improvements in continuous face aging capabilities compared to existing methods
- Technique enhances image fidelity, precision, and control over facial aging processes with promising implications for biometrics, forensics, and entertainment industry technologies.

## Summary

Significant advancements have been made in the field of face synthesis, particularly in the realm of face aging, through the utilization of generative adversarial networks (GANs). Traditional approaches to face aging often involve dividing datasets into distinct age groups and employing group-based training strategies. However, these methods lack the capability to offer fine-grained control over continuous aging synthesis. In their recent study titled "Continuous Face Aging via Self-estimated Residual Age Embedding," authors Zeqi Li, Ruowei Jiang, and Parham Aarabi introduce a novel approach that addresses this limitation. Their proposed unified network structure integrates a linear age estimator into a GAN-based model to accurately estimate the age of a given face image and provide personalized target age embeddings for both age progression and regression. This is achieved by incorporating two key components: personalized residual age embedding of the current age and exemplar-face aging basis of the target age derived from learned weights within the linear age estimator framework. Through qualitative and quantitative evaluations on various datasets, including those used in computer vision research conferences such as CVPR 2021, Li et al. demonstrate substantial improvements in continuous face aging capabilities compared to existing state-of-the-art methods. Their innovative technique not only enhances image fidelity but also offers enhanced precision and control over facial aging processes, showcasing promising implications for applications in fields such as biometrics, forensics, and entertainment industry technologies.