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Face Aging on Realistic Photos by Generative Adversarial Networks

Inspired by Gatys and Goodfellow's style transfer and generative adversarial network (GAN), we use CycleGAN to achieve age progression. CycleGAN is good at generating fake images and also competitive with other GANs. It not only generates fake images but also increases the number of images in our database. We know the better database, the better performance of the model. We also try a deeper generator to transform youth photos to elder photos. To avoid the artifacts, we not only adopt the idea of “cycle” but also add a new loss which can tell the discriminator not too strict to generated images. Finally, we collect images of young and old people from the Internet and use unsupervised learning to train our model. The experimental results show our proposed method is indeed improved and better than before.