  To detect bias in face recognition networks, it can be useful to probe a
network under test using samples in which only specific attributes vary in some
controlled way. However, capturing a sufficiently large dataset with specific
control over the attributes of interest is difficult. In this work, we describe
a simulator that applies specific head pose and facial expression adjustments
to images of previously unseen people. The simulator first fits a 3D morphable
model to a provided image, applies the desired head pose and facial expression
controls, then renders the model into an image. Next, a conditional Generative
Adversarial Network (GAN) conditioned on the original image and the rendered
morphable model is used to produce the image of the original person with the
new facial expression and head pose. We call this conditional GAN -- MorphGAN.
Images generated using MorphGAN conserve the identity of the person in the
original image, and the provided control over head pose and facial expression
allows test sets to be created to identify robustness issues of a facial
recognition deep network with respect to pose and expression. Images generated
by MorphGAN can also serve as data augmentation when training data are scarce.
We show that by augmenting small datasets of faces with new poses and
expressions improves the recognition performance by up to 9% depending on the
augmentation and data scarcity.
