Abstract
“If I provide you a face image of mine (without telling
you the actual age when I took the picture) and a large
amount of face images that I crawled (containing labeled
faces of different ages but not necessarily paired), can you
show me what I would look like when I am 80 or what I
was like when I was 5?” The answer is probably a “No.”
Most existing face aging works attempt to learn the transformation between age groups and thus would require the
paired samples as well as the labeled query image. In this
paper, we look at the problem from a generative modeling
perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can
directly produce the image with desired age attribute. We
propose a conditional adversarial autoencoder (CAAE) that
learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In
CAAE, the face is first mapped to a latent vector through
a convolutional encoder, and then the vector is projected
to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition
controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance
and flexibility of the proposed framework by comparing with
the state-of-the-art and ground truth.