  Micro-expressions (MEs) are brief and involuntary facial expressions that
occur when people are trying to hide their true feelings or conceal their
emotions. Based on psychology research, MEs play an important role in
understanding genuine emotions, which leads to many potential applications.
Therefore, ME analysis has become an attractive topic for various research
areas, such as psychology, law enforcement, and psychotherapy. In the computer
vision field, the study of MEs can be divided into two main tasks, spotting and
recognition, which are used to identify positions of MEs in videos and
determine the emotion category of the detected MEs, respectively. Recently,
although much research has been done, no fully automatic system for analyzing
MEs has yet been constructed on a practical level for two main reasons: most of
the research on MEs only focuses on the recognition part, while abandoning the
spotting task; current public datasets for ME spotting are not challenging
enough to support developing a robust spotting algorithm. The contributions of
this paper are threefold: (1) we introduce an extension of the SMIC-E database,
namely the SMIC-E-Long database, which is a new challenging benchmark for ME
spotting; (2) we suggest a new evaluation protocol that standardizes the
comparison of various ME spotting techniques; (3) extensive experiments with
handcrafted and deep learning-based approaches on the SMIC-E-Long database are
performed for baseline evaluation.
