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An important goal in human-robot-interaction (HRI) is for machines to achievea close to human level of face perception. One of the important differencesbetween machine learning and human intelligence is the lack ofcompositionality. This paper introduces a new scheme to enable generativeadversarial networks to learn the distribution of face images composed ofsmaller parts. This results in a more flexible machine face perception andeasier generalization to outside training examples. We demonstrate that thismodel is able to produce realistic high-quality face images by generating andpiecing together the parts. Additionally, we demonstrate that this model learnsthe relations between the facial parts and their distributions. Therefore, thespecific facial parts are interchangeable between generated face images.

Face Images as Jigsaw Puzzles: Compositional Perception of Human Faces for Machines Using Generative Adversarial Networks