  Micro-expressions (MEs) are rapid, involuntary facial expressions which
reveal emotions that people do not intend to show. Studying MEs is valuable as
recognizing them has many important applications, particularly in forensic
science and psychotherapy. However, analyzing spontaneous MEs is very
challenging due to their short duration and low intensity. Automatic ME
analysis includes two tasks: ME spotting and ME recognition. For ME spotting,
previous studies have focused on posed rather than spontaneous videos. For ME
recognition, the performance of previous studies is low. To address these
challenges, we make the following contributions: (i)We propose the first method
for spotting spontaneous MEs in long videos (by exploiting feature difference
contrast). This method is training free and works on arbitrary unseen videos.
(ii)We present an advanced ME recognition framework, which outperforms previous
work by a large margin on two challenging spontaneous ME databases (SMIC and
CASMEII). (iii)We propose the first automatic ME analysis system (MESR), which
can spot and recognize MEs from spontaneous video data. Finally, we show our
method outperforms humans in the ME recognition task by a large margin, and
achieves comparable performance to humans at the very challenging task of
spotting and then recognizing spontaneous MEs.
