  Facial aging and facial rejuvenation analyze a given face photograph to
predict a future look or estimate a past look of the person. To achieve this,
it is critical to preserve human identity and the corresponding aging
progression and regression with high accuracy. However, existing methods cannot
simultaneously handle these two objectives well. We propose a novel generative
adversarial network based approach, named the Conditional Multi-Adversarial
AutoEncoder with Ordinal Regression (CMAAE-OR). It utilizes an age estimation
technique to control the aging accuracy and takes a high-level feature
representation to preserve personalized identity. Specifically, the face is
first mapped to a latent vector through a convolutional encoder. The latent
vector is then projected onto the face manifold conditional on the age through
a deconvolutional generator. The latent vector preserves personalized face
features and the age controls facial aging and rejuvenation. A discriminator
and an ordinal regression are imposed on the encoder and the generator in
tandem, making the generated face images to be more photorealistic while
simultaneously exhibiting desirable aging effects. Besides, a high-level
feature representation is utilized to preserve personalized identity of the
generated face. Experiments on two benchmark datasets demonstrate appealing
performance of the proposed method over the state-of-the-art.
