  Micro-expression can reflect people's real emotions. Recognizing
micro-expressions is difficult because they are small motions and have a short
duration. As the research is deepening into micro-expression recognition, many
effective features and methods have been proposed. To determine which direction
of movement feature is easier for distinguishing micro-expressions, this paper
selects 18 directions (including three types of horizontal, vertical and
oblique movements) and proposes a new low-dimensional feature called the
Histogram of Single Direction Gradient (HSDG) to study this topic. In this
paper, HSDG in every direction is concatenated with LBP-TOP to obtain the LBP
with Single Direction Gradient (LBP-SDG) and analyze which direction of
movement feature is more discriminative for micro-expression recognition. As
with some existing work, Euler Video Magnification (EVM) is employed as a
preprocessing step. The experiments on the CASME II and SMIC-HS databases
summarize the effective and optimal directions and demonstrate that HSDG in an
optimal direction is discriminative, and the corresponding LBP-SDG achieves
state-of-the-art performance using EVM.
