10/5/2023 0 Comments Face age progression appSimulation results on five face datasets, namely IMDB-WIKI, CACD and UTKFace, FGNET, Celeb A are evaluated. Initially, Cycle-Generative Adversarial Network (CycleGAN) achieves the face age progression, further Enhanced Super-resolution Generative Adversarial Network (ESRGAN) automatically enhance the aged face image to improve the visibility. The generator produces fake images which are further differentiated by discriminator whether the image is real or fake. GAN has a generator and a discriminator network. To produce a realistic appearance with an enhanced vision of face image, a fusion-based Generative Adversarial Network approach is used. So, the proposed work is focused on these key issues using Generative Adversarial Networks (GANs). Several techniques are available for face age progression still identity preservation as well age estimation accuracy are big challenges and need attention.
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