  Capsule networks are designed to present the objects by a set of parts and
their relationships, which provide an insight into the procedure of visual
perception. Although recent works have shown the success of capsule networks on
simple objects like digits, the human faces with homologous structures, which
are suitable for capsules to describe, have not been explored. In this paper,
we propose a Hierarchical Parsing Capsule Network (HP-Capsule) for unsupervised
face subpart-part discovery. When browsing large-scale face images without
labels, the network first encodes the frequently observed patterns with a set
of explainable subpart capsules. Then, the subpart capsules are assembled into
part-level capsules through a Transformer-based Parsing Module (TPM) to learn
the compositional relations between them. During training, as the face
hierarchy is progressively built and refined, the part capsules adaptively
encode the face parts with semantic consistency. HP-Capsule extends the
application of capsule networks from digits to human faces and takes a step
forward to show how the neural networks understand homologous objects without
human intervention. Besides, HP-Capsule gives unsupervised face segmentation
results by the covered regions of part capsules, enabling qualitative and
quantitative evaluation. Experiments on BP4D and Multi-PIE datasets show the
effectiveness of our method.
