  Micro-expressions are rapid and involuntary facial expressions, which
indicate the suppressed or concealed emotions. Recently, the research on
automatic micro-expression (ME) spotting obtains increasing attention. ME
spotting is a crucial step prior to further ME analysis tasks. The spotting
results can be used as important cues to assist many other human-oriented tasks
and thus have many potential applications. In this paper, by investigating
existing ME spotting methods, we recognize the immediacy of standardizing the
performance evaluation of micro-expression spotting methods. To this end, we
construct a micro-expression spotting benchmark (MESB). Firstly, we set up a
sliding window based multi-scale evaluation framework. Secondly, we introduce a
series of protocols. Thirdly, we also provide baseline results of popular
methods. The MESB facilitates the research on ME spotting with fairer and more
comprehensive evaluation and also enables to leverage the cutting-edge machine
learning tools widely.
