Face Age

Face age research focuses on accurately modeling and manipulating the visual changes associated with human aging, primarily to improve the robustness of facial recognition systems across different age ranges. Current research employs deep learning models, including generative adversarial networks (GANs) like CycleGAN and novel architectures designed for multi-task learning, to synthesize realistic aged and de-aged faces and to develop age-invariant recognition algorithms. This work is crucial for enhancing the reliability of security systems, improving forensic applications, and addressing challenges in long-term biometric identification.