  This paper does not contain technical novelty but introduces our key
discoveries in a data generation protocol, a database and insights. We aim to
address the lack of large-scale datasets in micro-expression (MiE) recognition
due to the prohibitive cost of data collection, which renders large-scale
training less feasible. To this end, we develop a protocol to automatically
synthesize large scale MiE training data that allow us to train improved
recognition models for real-world test data. Specifically, we discover three
types of Action Units (AUs) that can constitute trainable MiEs. These AUs come
from real-world MiEs, early frames of macro-expression videos, and the
relationship between AUs and expression categories defined by human expert
knowledge. With these AUs, our protocol then employs large numbers of face
images of various identities and an off-the-shelf face generator for MiE
synthesis, yielding the MiE-X dataset. MiE recognition models are trained or
pre-trained on MiE-X and evaluated on real-world test sets, where very
competitive accuracy is obtained. Experimental results not only validate the
effectiveness of the discovered AUs and MiE-X dataset but also reveal some
interesting properties of MiEs: they generalize across faces, are close to
early-stage macro-expressions, and can be manually defined.
