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Facial aging and facial rejuvenation analyze a given face photograph topredict a future look or estimate a past look of the person. To achieve this,it is critical to preserve human identity and the corresponding agingprogression and regression with high accuracy. However, existing methods cannotsimultaneously handle these two objectives well. We propose a novel generativeadversarial network based approach, named the Conditional Multi-AdversarialAutoEncoder with Ordinal Regression (CMAAE-OR). It utilizes an age estimationtechnique to control the aging accuracy and takes a high-level featurerepresentation to preserve personalized identity. Specifically, the face isfirst mapped to a latent vector through a convolutional encoder. The latentvector is then projected onto the face manifold conditional on the age througha deconvolutional generator. The latent vector preserves personalized facefeatures and the age controls facial aging and rejuvenation. A discriminatorand an ordinal regression are imposed on the encoder and the generator intandem, making the generated face images to be more photorealistic whilesimultaneously exhibiting desirable aging effects. Besides, a high-levelfeature representation is utilized to preserve personalized identity of thegenerated face. Experiments on two benchmark datasets demonstrate appealingperformance of the proposed method over the state-of-the-art.

Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression