  In this paper, we propose a novel face synthesis approach that can generate
an arbitrarily large number of synthetic images of both real and synthetic
identities. Thus a face image dataset can be expanded in terms of the number of
identities represented and the number of images per identity using this
approach, without the identity-labeling and privacy complications that come
from downloading images from the web. To measure the visual fidelity and
uniqueness of the synthetic face images and identities, we conducted face
matching experiments with both human participants and a CNN pre-trained on a
dataset of 2.6M real face images. To evaluate the stability of these synthetic
faces, we trained a CNN model with an augmented dataset containing close to
200,000 synthetic faces. We used a snapshot of this trained CNN to recognize
extremely challenging frontal (real) face images. Experiments showed training
with the augmented faces boosted the face recognition performance of the CNN.
