  An important goal in human-robot-interaction (HRI) is for machines to achieve
a close to human level of face perception. One of the important differences
between machine learning and human intelligence is the lack of
compositionality. This paper introduces a new scheme to enable generative
adversarial networks to learn the distribution of face images composed of
smaller parts. This results in a more flexible machine face perception and
easier generalization to outside training examples. We demonstrate that this
model is able to produce realistic high-quality face images by generating and
piecing together the parts. Additionally, we demonstrate that this model learns
the relations between the facial parts and their distributions. Therefore, the
specific facial parts are interchangeable between generated face images.
