Abstract:
The facial features of human changes with age. It
is important to model the human face for cross-age verification
and recognition. In this paper, we introduce a Cycle Face Aging
Generative Adversarial Network (CFA-GANs) framework which
preserves original face identity in the aged version of his/her face.
Due to the shortage of paired data of human faces, we used CycleConsistent Generative Adversarial Network (CycleGANs) which
transform an image from source domain X to target domain Y in
absence of paired example. Our aim is to translate an input age
group image into target age group image for face aging problems.
Train on the various images, we demonstrate that our CFA-GAN
learns and transfer the features of the face from the input group
to target group. Results have been taken on UTKFace database
to obtain aged and regenerated face images.
