Face Aging with Improved Invertible Conditional GANs

Due to the continuous development of GAN, vivid faces can be generated, and the use of GAN for face aging becomes a novel trend. However, many existing works for face aging require tedious pre-processing of datasets. This brings a lot of computational burden and limits the application of face aging. In order to solve these problems, a face aging network is constructed using IcGAN without any data pre-processing which map a face image into personality and age vector spaces through encoders Z and Y. Different from the previous work, we make an emphasis on the preservation of both personalized and aging features. Thus, the minimize absolute reconstructing loss is proposed to optimize vector z, which can remain the personality characteristics, meanwhile preserving the pose, hairstyle and background of the input face. Additionally, we introduce a novel age vector optimization approach by classifying reconstruction loss and introduce the parameter λ which is well-balanced between large age features and subtle texture features. The experimental results demonstrate our proposed AlGAN provides better aging faces over other state-of-the-art age progression methods.