Authors - Vathsal Tammewar, Bharat Sharma, Dharti Sorte, R. Sreemathy Abstract - Accelerated facial aging using GANs has been the key interest area in generative modeling and facial analysis fields, which offers significant breakthrough in age progression and regression solutions. This survey conducted an extensive review on techniques of GAN- based approaches for accelerated facial aging, emphasizing highly realistic and controllable aging transformations. Many of these methods applied in forensic investigations, entertainment industries, or age-invariant facial recognition systems, which are vivid demonstrations of the versatility and practical relevance of such methods. While such recent breakthroughs hold great promises, several issues remain; namely high-fidelity transformations to preserve important facial details do not fully diminish biases due to imbalanced datasets, and temporal consistency when age progressions or regressions consist of sequential ages is also critical. Computational efficiency and real-time applicability are still the most critical areas of focus. This paper probes into the strengths, limitations, and open challenges of existing approaches, while emphasizing the importance of innovations such as improved loss functions, diverse and representative training datasets, and hybrid architectures. Thus, this survey contributes to synthesizing current progress and outlining future research directions for advancing the field of GAN-based facial aging technologies.
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