Prediction of face age progression with generative adversarial networks

Face age progression, goals to alter the individual’s face from a given face image to predict the future appearance of that image. In today’s world that demands more security and a touchless unique identification system, face aging attains tremendous attention. 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. Research has been continuing in face aging to handle the challenge to generate aged faces accurately. So, to solve the issue, the proposed work focuses on the realistic face aging method using AttentionGAN and SRGAN. AttentionGAN uses two separate subnets in a generator. One subnet for generating multiple attention masks and the other for generating multiple content masks. Then attention mask is multiplied with the corresponding content mask along with an input image to finally achieve the desired results. Further, the regex filtering process is performed to separates the synthesized face images from the output of AttentionGAN. Then image sharpening with edge enhancement is done to give high-quality input to SRGAN, which further generates the super-resolution face aged images. Thus, presents more detailed information in an image because of its high quality. Moreover, the experimental results are obtained from five publicly available datasets: UTKFace, CACD, FGNET, IMDB-WIKI, and CelebA. The proposed work is evaluated with quantitative and qualitative methods, produces synthesized face aged images with a 0.001% error rate, and is also evaluated with the comparison to prior methods. The paper focuses on the various practical applications of super-resolution face aging using Generative Adversarial Networks (GANs).