  Face aging is the task aiming to translate the faces in input images to
designated ages. To simplify the problem, previous methods have limited
themselves only able to produce discrete age groups, each of which consists of
ten years. Consequently, the exact ages of the translated results are unknown
and it is unable to obtain the faces of different ages within groups. To this
end, we propose the continuous face aging generative adversarial networks
(CFA-GAN). Specifically, to make the continuous aging feasible, we propose to
decompose image features into two orthogonal features: the identity and the age
basis features. Moreover, we introduce the novel loss function for identity
preservation which maximizes the cosine similarity between the original and the
generated identity basis features. With the qualitative and quantitative
evaluations on MORPH, we demonstrate the realistic and continuous aging ability
of our model, validating its superiority against existing models. To the best
of our knowledge, this work is the first attempt to handle continuous target
ages.
