  This paper presents baseline results for the first Micro-Expression Spotting
Challenge 2019 by evaluating local temporal pattern (LTP) on SAMM and CAS(ME)2.
The proposed LTP patterns are extracted by applying PCA in a temporal window on
several facial local regions. The micro-expression sequences are then spotted
by a local classification of LTP and a global fusion. The performance is
evaluated by Leave-One-Subject-Out cross validation. Furthermore, we define the
criteria of determining true positives in one video by overlap rate and set the
metric F1-score for spotting performance of the whole database. The F1-score of
baseline results for SAMM and CAS(ME)2 are 0.0316 and 0.0179, respectively.
