A NOVEL APPROACH FOR FACIAL AGING BASED ON CYCLEGAN

• Sayali Arya, Dr. Giirja G. Chiddarwar, Dr. Kalpana S. Thakre, Dr. Smita Chaudhari

Keywords: Face Aging, Generative Adversarial Networks, CycleGAN, Artificial Face, Deep Learning

ABSTRACT:-

Face aging is a fascinating and challenging task in the field of computer vision, with applications in entertainment, cosmetics, and forensics. In recent years, the CycleGAN framework has emerged as a powerful approach for face aging, enabling the transformation of facial images between different age groups without the need for paired training data. This abstract presents a unique exploration of face aging using CycleGAN, focusing on the generation of realistic and visually convincing aged faces from young faces. By leveraging the principles of generative adversarial networks (GAN) and cycle consistency, the proposed method captures and transfers the intricate facial details associated with aging, including wrinkles, fine lines, and skin texture changes. The training process involves optimizing the discriminator and generator components, with the training losses serving as crucial indicators of the model's convergence and learning progress. Through experiments and evaluations, the effectiveness and potential applications of the proposed approach are demonstrated, highlighting its contributions to the field of facial aging and its implications for various industries.