Survey On Cyclegan Based Face Aging Detection

As facial recognition systems increasingly permeate high-stakes domains such as security, healthcare, and forensics, their ability to handle age progression and regression becomes critical. However, changes in facial appearance due to aging often degrade recognition accuracy, prompting the emergence of synthetic face aging techniques as a potential solution. This survey explores state-of-the-art methods in synthetic facial aging, with a focus on their effectiveness in maintaining identity, realism, and applicability in age-invariant recognition systems. The paper reviews recent works addressing short- and long-term aging effects, fairness across demographics, feature fusion approaches, and the role of balanced datasets. Additionally, it examines the integration of loss functions and architectural enhancements that contribute to the realism and reliability of aged face generation. The review aims to contextualize current advancements, identify persistent challenges, and highlight promising directions for future research in synthetic aging and its integration with facial recognition systems.