  We provide a new non-invasive, easy-to-scale for large amounts of subjects
and a remotely accessible method for (hidden) emotion detection from videos of
human faces. Our approach combines face manifold detection for accurate
location of the face in the video with local face manifold embedding to create
a common domain for the measurements of muscle micro-movements that is
invariant to the movement of the subject in the video. In the next step, we
employ the Digital Image Speckle Correlation (DISC) and the optical flow
algorithm to compute the pattern of micro-movements in the face. The
corresponding vector field is mapped back to the original space and
superimposed on the original frames of the videos. Hence, the resulting videos
include additional information about the direction of the movement of the
muscles in the face. We take the publicly available CK++ dataset of visible
emotions and add to it videos of the same format but with hidden emotions. We
process all the videos using our micro-movement detection and use the results
to train a state-of-the-art network for emotions classification from videos --
Frame Attention Network (FAN). Although the original FAN model achieves very
high out-of-sample performance on the original CK++ videos, it does not perform
so well on hidden emotions videos. The performance improves significantly when
the model is trained and tested on videos with the vector fields of muscle
movements. Intuitively, the corresponding arrows serve as edges in the image
that are easily captured by the convolutions filters in the FAN network.
