# A research on cross-age facial recognition technology based on AT-GAN

## TL;DR

This paper introduces a new GAN-based framework for predicting how a person's face will look at different ages, improving accuracy and detail in age progression.

## Contribution

A novel cross-age facial recognition framework using AT-GAN with self-attention and semi-supervised learning for enhanced age prediction.

## Key findings

- The proposed framework achieves high prediction accuracy in cross-age facial recognition.
- The self-attention mechanism helps maintain consistent personal features across age predictions.
- The method shows strong generalization on both open-source and volunteer-provided datasets.

## Abstract

Currently, predicting a person’s facial appearance many years later based on early facial features remains a core technical challenge. In this paper, we propose a cross-age face prediction framework based on Generative Adversarial Networks (GANs). This framework extracts key features from early photos of the target individual and predicts their facial appearance at different ages in the future. Within our framework, we designed a GAN-based image restoration algorithm to enhance image deblurring capabilities and improve the generation of fine details, thereby increasing image resolution. Additionally, we introduced a semi-supervised learning algorithm called Multi-scale Feature Aggregation Scratch Repair (Semi-MSFA), which leverages both synthetic datasets and real historical photos to better adapt to the task of restoring old photographs. Furthermore, we developed a generative adversarial network incorporating a self-attention mechanism to predict age-progressed face images, ensuring the generated images maintain relatively stable personal characteristics across different ages. To validate the robustness and accuracy of our proposed framework, we conducted qualitative and quantitative analyses on open-source portrait databases and volunteer-provided data. Experimental results demonstrate that our framework achieves high prediction accuracy and strong generalization capabilities.