  In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN)
to explicitly deal with the large intra-class variations caused by
identity-related attributes, e.g., age, race, and gender. Specifically, a novel
PAT module with an associated PAT loss was proposed to learn features in a
hierarchical tree structure organized according to attributes, where the final
features are less affected by the attributes. Then, expression-related features
are extracted from leaf nodes. Samples are probabilistically assigned to tree
nodes at different levels such that expression-related features can be learned
from all samples weighted by probabilities. We further proposed a
semi-supervised strategy to learn the PAT-CNN from limited attribute-annotated
samples to make the best use of available data. Experimental results on five
facial expression datasets have demonstrated that the proposed PAT-CNN
outperforms the baseline models by explicitly modeling attributes. More
impressively, the PAT-CNN using a single model achieves the best performance
for faces in the wild on the SFEW dataset, compared with the state-of-the-art
methods using an ensemble of hundreds of CNNs.
