  To address the problem of data inconsistencies among different facial
expression recognition (FER) datasets, many cross-domain FER methods (CD-FERs)
have been extensively devised in recent years. Although each declares to
achieve superior performance, fair comparisons are lacking due to the
inconsistent choices of the source/target datasets and feature extractors. In
this work, we first analyze the performance effect caused by these inconsistent
choices, and then re-implement some well-performing CD-FER and recently
published domain adaptation algorithms. We ensure that all these algorithms
adopt the same source datasets and feature extractors for fair CD-FER
evaluations. We find that most of the current leading algorithms use
adversarial learning to learn holistic domain-invariant features to mitigate
domain shifts. However, these algorithms ignore local features, which are more
transferable across different datasets and carry more detailed content for
fine-grained adaptation. To address these issues, we integrate graph
representation propagation with adversarial learning for cross-domain
holistic-local feature co-adaptation by developing a novel adversarial graph
representation adaptation (AGRA) framework. Specifically, it first builds two
graphs to correlate holistic and local regions within each domain and across
different domains, respectively. Then, it extracts holistic-local features from
the input image and uses learnable per-class statistical distributions to
initialize the corresponding graph nodes. Finally, two stacked graph
convolution networks (GCNs) are adopted to propagate holistic-local features
within each domain to explore their interaction and across different domains
for holistic-local feature co-adaptation. We conduct extensive and fair
evaluations on several popular benchmarks and show that the proposed AGRA
framework outperforms previous state-of-the-art methods.
