  Micro-facial expressions are regarded as an important human behavioural event
that can highlight emotional deception. Spotting these movements is difficult
for humans and machines, however research into using computer vision to detect
subtle facial expressions is growing in popularity. This paper proposes an
individualised baseline micro-movement detection method using 3D Histogram of
Oriented Gradients (3D HOG) temporal difference method. We define a face
template consisting of 26 regions based on the Facial Action Coding System
(FACS). We extract the temporal features of each region using 3D HOG. Then, we
use Chi-square distance to find subtle facial motion in the local regions.
Finally, an automatic peak detector is used to detect micro-movements above the
newly proposed adaptive baseline threshold. The performance is validated on two
FACS coded datasets: SAMM and CASME II. This objective method focuses on the
movement of the 26 face regions. When comparing with the ground truth, the best
result was an AUC of 0.7512 and 0.7261 on SAMM and CASME II, respectively. The
results show that 3D HOG outperformed for micro-movement detection, compared to
state-of-the-art feature representations: Local Binary Patterns in Three
Orthogonal Planes and Histograms of Oriented Optical Flow.
