  Being spontaneous, micro-expressions are useful in the inference of a
person's true emotions even if an attempt is made to conceal them. Due to their
short duration and low intensity, the recognition of micro-expressions is a
difficult task in affective computing. The early work based on handcrafted
spatio-temporal features which showed some promise, has recently been
superseded by different deep learning approaches which now compete for the
state of the art performance. Nevertheless, the problem of capturing both local
and global spatio-temporal patterns remains challenging. To this end, herein we
propose a novel spatio-temporal transformer architecture -- to the best of our
knowledge, the first purely transformer based approach (i.e. void of any
convolutional network use) for micro-expression recognition. The architecture
comprises a spatial encoder which learns spatial patterns, a temporal
aggregator for temporal dimension analysis, and a classification head. A
comprehensive evaluation on three widely used spontaneous micro-expression data
sets, namely SMIC-HS, CASME II and SAMM, shows that the proposed approach
consistently outperforms the state of the art, and is the first framework in
the published literature on micro-expression recognition to achieve the
unweighted F1-score greater than 0.9 on any of the aforementioned data sets.
