AttentionGAN uses two separate subnets in a generator. So, to solve the issue, the proposed work focuses on the realistic face aging method using AttentionGAN and SRGAN. Research has been continuing in face aging to handle the challenge to generate aged faces accurately. The existing face age progression approaches have the key problem of unnatural modifications of facial attributes due to insufficient prior knowledge of input images and nearly visual artifacts in the generated output.
In today’s world that demands more security and a touchless unique identification system, face aging attains tremendous attention. Face age progression, goals to alter the individual’s face from a given face image to predict the future appearance of that image.
