  Composite-database micro-expression recognition is attracting increasing
attention as it is more practical to real-world applications. Though the
composite database provides more sample diversity for learning good
representation models, the important subtle dynamics are prone to disappearing
in the domain shift such that the models greatly degrade their performance,
especially for deep models. In this paper, we analyze the influence of learning
complexity, including the input complexity and model complexity, and discover
that the lower-resolution input data and shallower-architecture model are
helpful to ease the degradation of deep models in composite-database task.
Based on this, we propose a recurrent convolutional network (RCN) to explore
the shallower-architecture and lower-resolution input data, shrinking model and
input complexities simultaneously. Furthermore, we develop three parameter-free
modules (i.e., wide expansion, shortcut connection and attention unit) to
integrate with RCN without increasing any learnable parameters. These three
modules can enhance the representation ability in various perspectives while
preserving not-very-deep architecture for lower-resolution data. Besides, three
modules can further be combined by an automatic strategy (a neural architecture
search strategy) and the searched architecture becomes more robust. Extensive
experiments on MEGC2019 dataset (composited of existing SMIC, CASME II and SAMM
datasets) have verified the influence of learning complexity and shown that
RCNs with three modules and the searched combination outperform the
state-of-the-art approaches.
