September 10, 2025ITM Web of ConferencesOpen Access

Research on Face Age Synthesis Method

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Systematic review reveals deep learning methods like GAN improve face age synthesis, indicating ongoing challenges.

Current face age synthesis methods primarily utilize deep learning techniques, improving realism and accuracy.
Generative Adversarial Networks demonstrated significant advancements, enhancing detail fidelity in face age models.
Classical methods, including prototype-based strategies, laid the groundwork, while deep learning reshapes the landscape.
Key challenges include identity drift and high computational costs, necessitating further research on efficient architectures.

Wanru Zhu (2025) studied this question.

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