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We provide a new non-invasive, easy-to-scale for large amounts of subjectsand a remotely accessible method for (hidden) emotion detection from videos ofhuman faces. Our approach combines face manifold detection for accuratelocation of the face in the video with local face manifold embedding to createa common domain for the measurements of muscle micro-movements that isinvariant to the movement of the subject in the video. In the next step, weemploy the Digital Image Speckle Correlation (DISC) and the optical flowalgorithm to compute the pattern of micro-movements in the face. Thecorresponding vector field is mapped back to the original space andsuperimposed on the original frames of the videos. Hence, the resulting videosinclude additional information about the direction of the movement of themuscles in the face. We take the publicly available CK++ dataset of visibleemotions and add to it videos of the same format but with hidden emotions. Weprocess all the videos using our micro-movement detection and use the resultsto train a state-of-the-art network for emotions classification from videos --Frame Attention Network (FAN). Although the original FAN model achieves veryhigh out-of-sample performance on the original CK++ videos, it does not performso well on hidden emotions videos. The performance improves significantly whenthe model is trained and tested on videos with the vector fields of musclemovements. Intuitively, the corresponding arrows serve as edges in the imagethat are easily captured by the convolutions filters in the FAN network.

Detection of (Hidden) Emotions from Videos using Muscles Movements and Face Manifold Embedding