Abstract
Face aging is of great importance for cross-age recognition and entertainment related applications. However,
the lack of labeled faces of the same person across a long
age range makes it challenging. Because of different aging
speed of different persons, our face aging approach aims
at synthesizing a face whose target age lies in some given
age group instead of synthesizing a face with a certain age.
By grouping faces with target age together, the objective of
face aging is equivalent to transferring aging patterns of
faces within the target age group to the face whose aged
face is to be synthesized. Meanwhile, the synthesized face
should have the same identity with the input face. Thus we
propose an Identity-Preserved Conditional Generative Adversarial Networks (IPCGANs) framework, in which a Conditional Generative Adversarial Networks module functions
as generating a face that looks realistic and is with the target age, an identity-preserved module preserves the identity
information and an age classifier forces the generated face
with the target age. Both qualitative and quantitative experiments show that our method can generate more realistic faces in terms of image quality, person identity and age
consistency with human observations.